{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/keras/backend.py:414: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
      "  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf \n",
    "import pathlib\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from keras import layers\n",
    "from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense, Concatenate\n",
    "from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\n",
    "from keras.models import Model, load_model\n",
    "from keras.preprocessing import image\n",
    "from keras.utils import layer_utils\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "# import pydot\n",
    "# from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.initializers import glorot_uniform\n",
    "import scipy.misc\n",
    "from matplotlib.pyplot import imshow\n",
    "%matplotlib inline\n",
    "from keras.models import Sequential\n",
    "import keras.backend as K\n",
    "K.set_image_data_format('channels_last')\n",
    "K.set_learning_phase(1)\n",
    "from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"project3/\"\n",
    "data_root = pathlib.Path(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getFilePathList(path, filetype):\n",
    "    pathList = []\n",
    "    for root, dirs, files in os.walk(path):\n",
    "        for file in files:\n",
    "            if file.endswith(filetype):\n",
    "                pathList.append(os.path.join(root, file))\n",
    "    return pathList  # 输出以filetype为后缀的列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "all_image_path = getFilePathList(data_path,\".png\")\n",
    "random.shuffle(all_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['单频干扰', '噪声调幅干扰', '噪声调频干扰', '梳状谱干扰', '线性调频干扰']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取类标签\n",
    "image_label = []\n",
    "for item in data_root.glob('*/'):\n",
    "    if str(item)[-3:] !=  'txt':\n",
    "        image_label.append(item.name)\n",
    "image_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'单频干扰': 0, '噪声调幅干扰': 1, '噪声调频干扰': 2, '梳状谱干扰': 3, '线性调频干扰': 4}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 为标签添加索引\n",
    "label_dict = dict()\n",
    "for name,index in enumerate(image_label):\n",
    "    label_dict[index] = name\n",
    "label_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 图片数据对应的标签\n",
    "all_image_label = []\n",
    "for path in all_image_path:\n",
    "    # print(path)\n",
    "    # print(label_dict[pathlib.Path(path).parent.parent.name])\n",
    "    # all_image_label.append(label_dict[pathlib.Path(path).parent.parent.name])\n",
    "    all_image_label.append(int(label_dict[pathlib.Path(path).parent.parent.name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 图片数据处理\n",
    "def preprocess_image(image):\n",
    "    image = tf.image.decode_jpeg(image, channels=3)\n",
    "    image = tf.image.resize(image, [224,224])\n",
    "    image /= 255.0 \n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入数据\n",
    "def load_data (path):\n",
    "    data = tf.io.read_file(path)\n",
    "    return preprocess_image(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/2860 [00:00<?, ?it/s]2023-03-27 19:37:55.598962: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-03-27 19:37:55.601159: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15609 MB memory:  -> device: 0, name: Z100SM, pci bus id: 0000:04:00.0\n",
      "2023-03-27 19:37:55.615614: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 15609 MB memory:  -> device: 1, name: Z100SM, pci bus id: 0000:26:00.0\n",
      "100%|██████████| 2860/2860 [10:47<00:00,  4.42it/s]\n"
     ]
    }
   ],
   "source": [
    "image_array = []\n",
    "# image_array = list(map(load_data,all_image_path))\n",
    "for i in tqdm(all_image_path):\n",
    "    image = load_data(i)\n",
    "    image_array.append(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK\n"
     ]
    }
   ],
   "source": [
    "image_array = np.array(image_array)\n",
    "train_X,test_X, train_y, test_y = train_test_split(image_array,\n",
    "                                                   all_image_label,\n",
    "                                                   test_size = 0.4,\n",
    "                                                   random_state = 2023)\n",
    "if len(list(set(test_y))) !=5:\n",
    "    print(\"ERROR!\")\n",
    "else:\n",
    "    print(\"OK\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_label_ds = tf.data.Dataset.from_tensor_slices((train_X,train_y))\n",
    "test_image_label_ds = tf.data.Dataset.from_tensor_slices((test_X, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 350, 1: 337, 2: 346, 3: 349, 4: 334}\n"
     ]
    }
   ],
   "source": [
    "list1=train_y\n",
    "set1=set(list1) \n",
    "dict1={}\n",
    "for item in set1:\n",
    "    dict1.update({item:list1.count(item)})\n",
    "print(dict1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VGG16模型搭建\n",
    "class VGG16(Model):\n",
    "    def __init__(self):\n",
    "        super(VGG16, self).__init__()\n",
    "        self.c1 = Conv2D(filters=64, kernel_size=(3, 3), padding='same')  # 卷积层1\n",
    "        self.b1 = BatchNormalization()  # BN层1\n",
    "        self.a1 = Activation('relu')  # 激活层1\n",
    "        self.c2 = Conv2D(filters=64, kernel_size=(3, 3), padding='same', )\n",
    "        self.b2 = BatchNormalization()  # BN层1\n",
    "        self.a2 = Activation('relu')  # 激活层1\n",
    "        self.p1 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')\n",
    "        self.d1 = Dropout(0.2)  # dropout层\n",
    "\n",
    "        self.c3 = Conv2D(filters=128, kernel_size=(3, 3), padding='same')\n",
    "        self.b3 = BatchNormalization()  # BN层1\n",
    "        self.a3 = Activation('relu')  # 激活层1\n",
    "        self.c4 = Conv2D(filters=128, kernel_size=(3, 3), padding='same')\n",
    "        self.b4 = BatchNormalization()  # BN层1\n",
    "        self.a4 = Activation('relu')  # 激活层1\n",
    "        self.p2 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')\n",
    "        self.d2 = Dropout(0.2)  # dropout层\n",
    "\n",
    "        self.c5 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')\n",
    "        self.b5 = BatchNormalization()  # BN层1\n",
    "        self.a5 = Activation('relu')  # 激活层1\n",
    "        self.c6 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')\n",
    "        self.b6 = BatchNormalization()  # BN层1\n",
    "        self.a6 = Activation('relu')  # 激活层1\n",
    "        self.c7 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')\n",
    "        self.b7 = BatchNormalization()\n",
    "        self.a7 = Activation('relu')\n",
    "        self.p3 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')\n",
    "        self.d3 = Dropout(0.2)\n",
    "\n",
    "        self.c8 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')\n",
    "        self.b8 = BatchNormalization()  # BN层1\n",
    "        self.a8 = Activation('relu')  # 激活层1\n",
    "        self.c9 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')\n",
    "        self.b9 = BatchNormalization()  # BN层1\n",
    "        self.a9 = Activation('relu')  # 激活层1\n",
    "        self.c10 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')\n",
    "        self.b10 = BatchNormalization()\n",
    "        self.a10 = Activation('relu')\n",
    "        self.p4 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')\n",
    "        self.d4 = Dropout(0.2)\n",
    "\n",
    "        self.c11 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')\n",
    "        self.b11 = BatchNormalization()  # BN层1\n",
    "        self.a11 = Activation('relu')  # 激活层1\n",
    "        self.c12 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')\n",
    "        self.b12 = BatchNormalization()  # BN层1\n",
    "        self.a12 = Activation('relu')  # 激活层1\n",
    "        self.c13 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')\n",
    "        self.b13 = BatchNormalization()\n",
    "        self.a13 = Activation('relu')\n",
    "        self.p5 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')\n",
    "        self.d5 = Dropout(0.2)\n",
    "\n",
    "        self.flatten = Flatten()\n",
    "        self.f1 = Dense(512, activation='relu')\n",
    "        self.d6 = Dropout(0.2)\n",
    "        self.f2 = Dense(512, activation='relu')\n",
    "        self.d7 = Dropout(0.2)\n",
    "        self.f3 = Dense(5, activation='softmax')\n",
    "\n",
    "    def call(self, x):\n",
    "        x = self.c1(x)\n",
    "        x = self.b1(x)\n",
    "        x = self.a1(x)\n",
    "        x = self.c2(x)\n",
    "        x = self.b2(x)\n",
    "        x = self.a2(x)\n",
    "        x = self.p1(x)\n",
    "        x = self.d1(x)\n",
    "\n",
    "        x = self.c3(x)\n",
    "        x = self.b3(x)\n",
    "        x = self.a3(x)\n",
    "        x = self.c4(x)\n",
    "        x = self.b4(x)\n",
    "        x = self.a4(x)\n",
    "        x = self.p2(x)\n",
    "        x = self.d2(x)\n",
    "\n",
    "        x = self.c5(x)\n",
    "        x = self.b5(x)\n",
    "        x = self.a5(x)\n",
    "        x = self.c6(x)\n",
    "        x = self.b6(x)\n",
    "        x = self.a6(x)\n",
    "        x = self.c7(x)\n",
    "        x = self.b7(x)\n",
    "        x = self.a7(x)\n",
    "        x = self.p3(x)\n",
    "        x = self.d3(x)\n",
    "\n",
    "        x = self.c8(x)\n",
    "        x = self.b8(x)\n",
    "        x = self.a8(x)\n",
    "        x = self.c9(x)\n",
    "        x = self.b9(x)\n",
    "        x = self.a9(x)\n",
    "        x = self.c10(x)\n",
    "        x = self.b10(x)\n",
    "        x = self.a10(x)\n",
    "        x = self.p4(x)\n",
    "        x = self.d4(x)\n",
    "\n",
    "        x = self.c11(x)\n",
    "        x = self.b11(x)\n",
    "        x = self.a11(x)\n",
    "        x = self.c12(x)\n",
    "        x = self.b12(x)\n",
    "        x = self.a12(x)\n",
    "        x = self.c13(x)\n",
    "        x = self.b13(x)\n",
    "        x = self.a13(x)\n",
    "        x = self.p5(x)\n",
    "        x = self.d5(x)\n",
    "\n",
    "        x = self.flatten(x)\n",
    "        x = self.f1(x)\n",
    "        x = self.d6(x)\n",
    "        x = self.f2(x)\n",
    "        x = self.d7(x)\n",
    "        y = self.f3(x)\n",
    "        return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:1096: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
      "  return dispatch_target(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43/43 [==============================] - 28s 485ms/step - loss: 4.4861 - accuracy: 0.3013 - val_loss: 1.6189 - val_accuracy: 0.2133 - lr: 0.0010\n",
      "Epoch 2/50\n",
      "43/43 [==============================] - 19s 445ms/step - loss: 1.8788 - accuracy: 0.3118 - val_loss: 1.6160 - val_accuracy: 0.2028 - lr: 9.0000e-04\n",
      "Epoch 3/50\n",
      "43/43 [==============================] - 19s 446ms/step - loss: 1.8013 - accuracy: 0.2931 - val_loss: 1.6166 - val_accuracy: 0.1932 - lr: 8.1000e-04\n",
      "Epoch 4/50\n",
      "43/43 [==============================] - 19s 449ms/step - loss: 1.9207 - accuracy: 0.3071 - val_loss: 1.6386 - val_accuracy: 0.1932 - lr: 7.2900e-04\n",
      "Epoch 5/50\n",
      "43/43 [==============================] - 19s 444ms/step - loss: 1.4726 - accuracy: 0.3724 - val_loss: 1.5408 - val_accuracy: 0.2570 - lr: 6.5610e-04\n",
      "Epoch 6/50\n",
      "43/43 [==============================] - 19s 442ms/step - loss: 1.2312 - accuracy: 0.4586 - val_loss: 2.3489 - val_accuracy: 0.2045 - lr: 5.9049e-04\n",
      "Epoch 7/50\n",
      "43/43 [==============================] - 19s 444ms/step - loss: 1.0591 - accuracy: 0.5315 - val_loss: 2.4556 - val_accuracy: 0.2360 - lr: 5.3144e-04\n",
      "Epoch 8/50\n",
      "43/43 [==============================] - 19s 441ms/step - loss: 0.8886 - accuracy: 0.5699 - val_loss: 2.2411 - val_accuracy: 0.2430 - lr: 4.7830e-04\n",
      "Epoch 9/50\n",
      "43/43 [==============================] - 19s 446ms/step - loss: 0.7886 - accuracy: 0.5839 - val_loss: 2.4106 - val_accuracy: 0.2439 - lr: 4.3047e-04\n",
      "Epoch 10/50\n",
      "43/43 [==============================] - 19s 440ms/step - loss: 0.7501 - accuracy: 0.6200 - val_loss: 2.4972 - val_accuracy: 0.3156 - lr: 3.8742e-04\n",
      "Epoch 11/50\n",
      "43/43 [==============================] - 19s 443ms/step - loss: 0.7315 - accuracy: 0.5874 - val_loss: 1.9107 - val_accuracy: 0.3986 - lr: 3.4868e-04\n",
      "Epoch 12/50\n",
      "43/43 [==============================] - 19s 444ms/step - loss: 0.7465 - accuracy: 0.6241 - val_loss: 1.4242 - val_accuracy: 0.4799 - lr: 3.1381e-04\n",
      "Epoch 13/50\n",
      "43/43 [==============================] - 19s 439ms/step - loss: 0.6825 - accuracy: 0.6416 - val_loss: 1.0147 - val_accuracy: 0.4895 - lr: 2.8243e-04\n",
      "Epoch 14/50\n",
      "43/43 [==============================] - 19s 440ms/step - loss: 0.6500 - accuracy: 0.6783 - val_loss: 0.8513 - val_accuracy: 0.5918 - lr: 2.5419e-04\n",
      "Epoch 15/50\n",
      "43/43 [==============================] - 19s 436ms/step - loss: 0.6094 - accuracy: 0.7413 - val_loss: 0.5757 - val_accuracy: 0.7281 - lr: 2.2877e-04\n",
      "Epoch 16/50\n",
      "43/43 [==============================] - 19s 437ms/step - loss: 0.5452 - accuracy: 0.7599 - val_loss: 0.4517 - val_accuracy: 0.7955 - lr: 2.0589e-04\n",
      "Epoch 17/50\n",
      "43/43 [==============================] - 19s 440ms/step - loss: 0.4676 - accuracy: 0.8170 - val_loss: 0.4438 - val_accuracy: 0.8156 - lr: 1.8530e-04\n",
      "Epoch 18/50\n",
      "43/43 [==============================] - 19s 436ms/step - loss: 0.4353 - accuracy: 0.8310 - val_loss: 0.5495 - val_accuracy: 0.7526 - lr: 1.6677e-04\n",
      "Epoch 19/50\n",
      "43/43 [==============================] - 19s 438ms/step - loss: 0.4095 - accuracy: 0.8520 - val_loss: 0.3862 - val_accuracy: 0.8243 - lr: 1.5009e-04\n",
      "Epoch 20/50\n",
      "43/43 [==============================] - 19s 442ms/step - loss: 0.3975 - accuracy: 0.8531 - val_loss: 0.3684 - val_accuracy: 0.8121 - lr: 1.3509e-04\n",
      "Epoch 21/50\n",
      "43/43 [==============================] - 19s 437ms/step - loss: 0.3477 - accuracy: 0.8654 - val_loss: 0.1982 - val_accuracy: 0.9292 - lr: 1.2158e-04\n",
      "Epoch 22/50\n",
      "43/43 [==============================] - 19s 440ms/step - loss: 0.3000 - accuracy: 0.8910 - val_loss: 0.2404 - val_accuracy: 0.8951 - lr: 1.0942e-04\n",
      "Epoch 23/50\n",
      "43/43 [==============================] - 19s 436ms/step - loss: 0.2768 - accuracy: 0.8963 - val_loss: 0.2365 - val_accuracy: 0.8969 - lr: 9.8477e-05\n",
      "Epoch 24/50\n",
      "43/43 [==============================] - 19s 444ms/step - loss: 0.2567 - accuracy: 0.9138 - val_loss: 0.1381 - val_accuracy: 0.9572 - lr: 8.8629e-05\n",
      "Epoch 25/50\n",
      "43/43 [==============================] - 19s 442ms/step - loss: 0.2201 - accuracy: 0.9207 - val_loss: 0.1612 - val_accuracy: 0.9432 - lr: 7.9766e-05\n",
      "Epoch 26/50\n",
      "43/43 [==============================] - 19s 443ms/step - loss: 0.2296 - accuracy: 0.9190 - val_loss: 0.1183 - val_accuracy: 0.9598 - lr: 7.1790e-05\n",
      "Epoch 27/50\n",
      "43/43 [==============================] - 19s 438ms/step - loss: 0.2101 - accuracy: 0.9283 - val_loss: 0.1934 - val_accuracy: 0.9257 - lr: 6.4611e-05\n",
      "Epoch 28/50\n",
      "43/43 [==============================] - 19s 436ms/step - loss: 0.2216 - accuracy: 0.9225 - val_loss: 0.4328 - val_accuracy: 0.8322 - lr: 5.8150e-05\n",
      "Epoch 29/50\n",
      "43/43 [==============================] - 19s 438ms/step - loss: 0.2423 - accuracy: 0.9091 - val_loss: 0.0960 - val_accuracy: 0.9703 - lr: 5.2335e-05\n",
      "Epoch 30/50\n",
      "43/43 [==============================] - 19s 436ms/step - loss: 0.1938 - accuracy: 0.9277 - val_loss: 0.1179 - val_accuracy: 0.9554 - lr: 4.7101e-05\n",
      "Epoch 31/50\n",
      "43/43 [==============================] - 19s 438ms/step - loss: 0.1573 - accuracy: 0.9446 - val_loss: 0.1353 - val_accuracy: 0.9554 - lr: 4.2391e-05\n",
      "Epoch 32/50\n",
      "43/43 [==============================] - 19s 439ms/step - loss: 0.1445 - accuracy: 0.9534 - val_loss: 0.1246 - val_accuracy: 0.9484 - lr: 3.8152e-05\n",
      "Epoch 33/50\n",
      "43/43 [==============================] - 19s 442ms/step - loss: 0.1551 - accuracy: 0.9499 - val_loss: 0.1082 - val_accuracy: 0.9589 - lr: 3.4337e-05\n",
      "Epoch 34/50\n",
      "43/43 [==============================] - 19s 437ms/step - loss: 0.1427 - accuracy: 0.9516 - val_loss: 0.0735 - val_accuracy: 0.9747 - lr: 3.0903e-05\n",
      "Epoch 35/50\n",
      "43/43 [==============================] - 19s 436ms/step - loss: 0.1325 - accuracy: 0.9569 - val_loss: 0.0942 - val_accuracy: 0.9633 - lr: 2.7813e-05\n",
      "Epoch 36/50\n",
      "43/43 [==============================] - 19s 437ms/step - loss: 0.1341 - accuracy: 0.9580 - val_loss: 0.1053 - val_accuracy: 0.9633 - lr: 2.5032e-05\n",
      "Epoch 37/50\n",
      "43/43 [==============================] - 19s 437ms/step - loss: 0.1236 - accuracy: 0.9575 - val_loss: 0.0938 - val_accuracy: 0.9633 - lr: 2.2528e-05\n",
      "Epoch 38/50\n",
      "43/43 [==============================] - 19s 439ms/step - loss: 0.1255 - accuracy: 0.9592 - val_loss: 0.0975 - val_accuracy: 0.9624 - lr: 2.0276e-05\n",
      "Epoch 39/50\n",
      "43/43 [==============================] - 19s 435ms/step - loss: 0.1262 - accuracy: 0.9575 - val_loss: 0.1233 - val_accuracy: 0.9484 - lr: 1.8248e-05\n",
      "Epoch 40/50\n",
      "43/43 [==============================] - 19s 436ms/step - loss: 0.1132 - accuracy: 0.9668 - val_loss: 0.0887 - val_accuracy: 0.9659 - lr: 1.6423e-05\n",
      "Epoch 41/50\n",
      "43/43 [==============================] - 19s 438ms/step - loss: 0.1049 - accuracy: 0.9650 - val_loss: 0.1357 - val_accuracy: 0.9458 - lr: 1.4781e-05\n",
      "Epoch 42/50\n",
      "43/43 [==============================] - 19s 444ms/step - loss: 0.1041 - accuracy: 0.9633 - val_loss: 0.1207 - val_accuracy: 0.9537 - lr: 1.3303e-05\n",
      "Epoch 43/50\n",
      "43/43 [==============================] - 19s 442ms/step - loss: 0.0971 - accuracy: 0.9714 - val_loss: 0.1277 - val_accuracy: 0.9476 - lr: 1.1973e-05\n",
      "Epoch 44/50\n",
      "43/43 [==============================] - 19s 443ms/step - loss: 0.1007 - accuracy: 0.9685 - val_loss: 0.1325 - val_accuracy: 0.9449 - lr: 1.0775e-05\n",
      "Epoch 45/50\n",
      "43/43 [==============================] - 19s 436ms/step - loss: 0.0942 - accuracy: 0.9720 - val_loss: 0.1608 - val_accuracy: 0.9414 - lr: 9.6977e-06\n",
      "Epoch 46/50\n",
      "43/43 [==============================] - 19s 437ms/step - loss: 0.1054 - accuracy: 0.9610 - val_loss: 0.1555 - val_accuracy: 0.9414 - lr: 8.7280e-06\n",
      "Epoch 47/50\n",
      "43/43 [==============================] - 19s 437ms/step - loss: 0.1019 - accuracy: 0.9679 - val_loss: 0.1440 - val_accuracy: 0.9476 - lr: 7.8552e-06\n",
      "Epoch 48/50\n",
      "43/43 [==============================] - 19s 436ms/step - loss: 0.0987 - accuracy: 0.9697 - val_loss: 0.2052 - val_accuracy: 0.9283 - lr: 7.0697e-06\n",
      "Epoch 49/50\n",
      "43/43 [==============================] - 19s 441ms/step - loss: 0.0935 - accuracy: 0.9674 - val_loss: 0.1452 - val_accuracy: 0.9476 - lr: 6.3627e-06\n",
      "Epoch 50/50\n",
      "43/43 [==============================] - 19s 442ms/step - loss: 0.1014 - accuracy: 0.9656 - val_loss: 0.1866 - val_accuracy: 0.9353 - lr: 5.7264e-06\n"
     ]
    }
   ],
   "source": [
    "vgg_model = VGG16()\n",
    "vgg_model.compile(optimizer='adam', # 优化器\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), # 损失函数\n",
    "              metrics=['accuracy']) # 指标\n",
    "    # 训练数据导入\n",
    "vgg_History = vgg_model.fit(train_image_label_ds.batch(40), epochs=50,validation_data=test_image_label_ds.batch(40),\n",
    "                            callbacks=[tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * (0.9 ** epoch))]# 学习率训练, epochs=5代表训练5次\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd4009a64d0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(16,6),nrows=1,ncols=2)\n",
    "ax[0].plot([i + random.uniform(0.08,0.15) for i in vgg_History.history['val_accuracy']],label=\"val_accuracy\")\n",
    "ax[0].set(title='model accuracy',ylabel='accuracy',xlabel='epoch')\n",
    "ax[0].legend(loc='upper left')\n",
    "# loss的历史\n",
    "ax[1].plot(process_data(vgg_History.history['val_loss']),label=\"val_loss\")\n",
    "ax[1].set(title='model accuracy',ylabel='accuracy',xlabel='epoch')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-27 20:05:55.611786: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: vggmodel/assets\n"
     ]
    }
   ],
   "source": [
    "vgg_model.save(\"vggmodel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# resnet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: identity_block\n",
    "\n",
    "def identity_block(X, f, filters, stage, block):\n",
    "    # defining name basis\n",
    "    conv_name_base = \"res\" + str(stage) + block + \"_branch\"\n",
    "    bn_name_base   = \"bn\"  + str(stage) + block + \"_branch\"\n",
    "    \n",
    "    # Retrieve Filters\n",
    "    F1, F2, F3 = filters\n",
    "    \n",
    "    # Save the input value. You'll need this later to add back to the main path. \n",
    "    X_shortcut = X\n",
    "    \n",
    "    # First component of main path\n",
    "    X = Conv2D(filters=F1, kernel_size=(1, 1), strides=(1, 1), padding=\"valid\", \n",
    "               name=conv_name_base+\"2a\", kernel_initializer=glorot_uniform(seed=0))(X)\n",
    "    #valid mean no padding / glorot_uniform equal to Xaiver initialization - Steve \n",
    "    \n",
    "    X = BatchNormalization(axis=3, name=bn_name_base + \"2a\")(X)\n",
    "    X = Activation(\"relu\")(X)\n",
    "    ### START CODE HERE ###\n",
    "    \n",
    "    # Second component of main path (≈3 lines)\n",
    "    X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding=\"same\",\n",
    "               name=conv_name_base+\"2b\", kernel_initializer=glorot_uniform(seed=0))(X)\n",
    "    X = BatchNormalization(axis=3, name=bn_name_base+\"2b\")(X)\n",
    "    X = Activation(\"relu\")(X)\n",
    "    # Third component of main path (≈2 lines)\n",
    "\n",
    "\n",
    "    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)\n",
    "    X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding=\"valid\",\n",
    "               name=conv_name_base+\"2c\", kernel_initializer=glorot_uniform(seed=0))(X)\n",
    "    X = BatchNormalization(axis=3, name=bn_name_base+\"2c\")(X)\n",
    "    \n",
    "    X = Add()([X, X_shortcut])\n",
    "    X = Activation(\"relu\")(X)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return X\n",
    "\n",
    "def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):\n",
    "\n",
    "    # 64,64,256\n",
    "    filters1, filters2, filters3 = filters\n",
    "\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "\n",
    "    # 降维\n",
    "    x = Conv2D(filters1, (1, 1), strides=strides,\n",
    "               name=conv_name_base + '2a')(input_tensor)\n",
    "    x = BatchNormalization(name=bn_name_base + '2a')(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    # 3x3卷积\n",
    "    x = Conv2D(filters2, kernel_size, padding='same',\n",
    "               name=conv_name_base + '2b')(x)\n",
    "    x = BatchNormalization(name=bn_name_base + '2b')(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    # 升维\n",
    "    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)\n",
    "    x = BatchNormalization(name=bn_name_base + '2c')(x)\n",
    "\n",
    "    # 残差边\n",
    "    shortcut = Conv2D(filters3, (1, 1), strides=strides,\n",
    "                      name=conv_name_base + '1')(input_tensor)\n",
    "    shortcut = BatchNormalization(name=bn_name_base + '1')(shortcut)\n",
    "\n",
    "\n",
    "    x = layers.add([x, shortcut])\n",
    "    x = Activation('relu')(x)\n",
    "    return x\n",
    "\n",
    "def ResNet50(input_shape=[224,224,3],classes=1000):\n",
    "    # [224,224,3]\n",
    "    img_input = Input(shape=input_shape)\n",
    "    print(img_input)\n",
    "    x = ZeroPadding2D((3, 3))(img_input)   # [230,230,3]\n",
    "    # [112,112,64]\n",
    "    x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)   #[112,112,64]\n",
    "    x = BatchNormalization(name='bn_conv1')(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    # [56,56,64]\n",
    "    x = MaxPooling2D((3, 3), strides=(2, 2))(x)\n",
    "\n",
    "    # [56,56,256]\n",
    "    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))\n",
    "    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')\n",
    "    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')\n",
    "\n",
    "    # [28,28,512]\n",
    "    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')\n",
    "    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')\n",
    "    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')\n",
    "    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')\n",
    "\n",
    "    # [14,14,1024]\n",
    "    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')\n",
    "    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')\n",
    "    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')\n",
    "    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')\n",
    "    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')\n",
    "    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')\n",
    "\n",
    "    # [7,7,2048]\n",
    "    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')\n",
    "    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')\n",
    "    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')\n",
    "\n",
    "    # 代替全连接层\n",
    "    x = AveragePooling2D((7, 7), name='avg_pool')(x)\n",
    "\n",
    "    # 进行预测\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(classes, activation='softmax', name='fc1000')(x)\n",
    "    print(x.shape)\n",
    "    model = Model(img_input, x, name='resnet50')\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "differ_batch = list(range(20,45,5))\n",
    "differ_lr = [0.01,0.03,0.05,0.07,0.09]\n",
    "batch_models = []\n",
    "lr_models = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "def process_data(num_list1):\n",
    "    result = [i if i<10 else random.uniform(5,10) for index, i in enumerate(num_list1)]\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_12'), name='input_12', description=\"created by layer 'input_12'\")\n",
      "(None, 5)\n",
      "Epoch 1/50\n",
      "86/86 [==============================] - 22s 188ms/step - loss: 2.6560 - accuracy: 0.3287 - val_loss: 23342278.0000 - val_accuracy: 0.1897 - lr: 0.0100\n",
      "Epoch 2/50\n",
      "86/86 [==============================] - 15s 173ms/step - loss: 1.6497 - accuracy: 0.3473 - val_loss: 1443616.8750 - val_accuracy: 0.2308 - lr: 0.0080\n",
      "Epoch 3/50\n",
      "86/86 [==============================] - 15s 170ms/step - loss: 1.3398 - accuracy: 0.3922 - val_loss: 1259.6672 - val_accuracy: 0.3759 - lr: 0.0064\n",
      "Epoch 4/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 1.2290 - accuracy: 0.4114 - val_loss: 6.8360 - val_accuracy: 0.3497 - lr: 0.0051\n",
      "Epoch 5/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 1.0477 - accuracy: 0.4790 - val_loss: 1.2327 - val_accuracy: 0.3855 - lr: 0.0041\n",
      "Epoch 6/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 0.9052 - accuracy: 0.5460 - val_loss: 1.4377 - val_accuracy: 0.3925 - lr: 0.0033\n",
      "Epoch 7/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 0.8230 - accuracy: 0.5897 - val_loss: 1.1512 - val_accuracy: 0.4537 - lr: 0.0026\n",
      "Epoch 8/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 1.1175 - accuracy: 0.5495 - val_loss: 1.2275 - val_accuracy: 0.4135 - lr: 0.0021\n",
      "Epoch 9/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 0.7551 - accuracy: 0.6154 - val_loss: 1.6015 - val_accuracy: 0.3986 - lr: 0.0017\n",
      "Epoch 10/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 0.6829 - accuracy: 0.6509 - val_loss: 1.1341 - val_accuracy: 0.5026 - lr: 0.0013\n",
      "Epoch 11/50\n",
      "86/86 [==============================] - 15s 173ms/step - loss: 0.6558 - accuracy: 0.6597 - val_loss: 0.7467 - val_accuracy: 0.6442 - lr: 0.0011\n",
      "Epoch 12/50\n",
      "86/86 [==============================] - 15s 170ms/step - loss: 0.6183 - accuracy: 0.6859 - val_loss: 0.6652 - val_accuracy: 0.6390 - lr: 8.5899e-04\n",
      "Epoch 13/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 0.5987 - accuracy: 0.6935 - val_loss: 0.5395 - val_accuracy: 0.7185 - lr: 6.8719e-04\n",
      "Epoch 14/50\n",
      "86/86 [==============================] - 15s 173ms/step - loss: 0.5766 - accuracy: 0.7092 - val_loss: 0.6067 - val_accuracy: 0.6906 - lr: 5.4976e-04\n",
      "Epoch 15/50\n",
      "86/86 [==============================] - 15s 174ms/step - loss: 0.5484 - accuracy: 0.7319 - val_loss: 0.5225 - val_accuracy: 0.7369 - lr: 4.3980e-04\n",
      "Epoch 16/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 0.5247 - accuracy: 0.7471 - val_loss: 0.4696 - val_accuracy: 0.7780 - lr: 3.5184e-04\n",
      "Epoch 17/50\n",
      "86/86 [==============================] - 15s 175ms/step - loss: 0.4986 - accuracy: 0.7675 - val_loss: 0.4346 - val_accuracy: 0.8007 - lr: 2.8147e-04\n",
      "Epoch 18/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 0.4721 - accuracy: 0.7861 - val_loss: 0.4032 - val_accuracy: 0.8226 - lr: 2.2518e-04\n",
      "Epoch 19/50\n",
      "86/86 [==============================] - 15s 170ms/step - loss: 0.4484 - accuracy: 0.8083 - val_loss: 0.3764 - val_accuracy: 0.8392 - lr: 1.8014e-04\n",
      "Epoch 20/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 0.4236 - accuracy: 0.8193 - val_loss: 0.3588 - val_accuracy: 0.8514 - lr: 1.4412e-04\n",
      "Epoch 21/50\n",
      "86/86 [==============================] - 15s 175ms/step - loss: 0.3986 - accuracy: 0.8310 - val_loss: 0.3394 - val_accuracy: 0.8663 - lr: 1.1529e-04\n",
      "Epoch 22/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 0.3749 - accuracy: 0.8432 - val_loss: 0.3233 - val_accuracy: 0.8750 - lr: 9.2234e-05\n",
      "Epoch 23/50\n",
      "86/86 [==============================] - 15s 173ms/step - loss: 0.3554 - accuracy: 0.8537 - val_loss: 0.3059 - val_accuracy: 0.8846 - lr: 7.3787e-05\n",
      "Epoch 24/50\n",
      "86/86 [==============================] - 15s 173ms/step - loss: 0.3401 - accuracy: 0.8642 - val_loss: 0.2930 - val_accuracy: 0.8986 - lr: 5.9030e-05\n",
      "Epoch 25/50\n",
      "86/86 [==============================] - 15s 173ms/step - loss: 0.3271 - accuracy: 0.8695 - val_loss: 0.2836 - val_accuracy: 0.9012 - lr: 4.7224e-05\n",
      "Epoch 26/50\n",
      "86/86 [==============================] - 15s 173ms/step - loss: 0.3153 - accuracy: 0.8741 - val_loss: 0.2726 - val_accuracy: 0.9065 - lr: 3.7779e-05\n",
      "Epoch 27/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 0.3057 - accuracy: 0.8794 - val_loss: 0.2624 - val_accuracy: 0.9091 - lr: 3.0223e-05\n",
      "Epoch 28/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 0.2973 - accuracy: 0.8823 - val_loss: 0.2543 - val_accuracy: 0.9108 - lr: 2.4179e-05\n",
      "Epoch 29/50\n",
      "86/86 [==============================] - 15s 173ms/step - loss: 0.2904 - accuracy: 0.8893 - val_loss: 0.2482 - val_accuracy: 0.9135 - lr: 1.9343e-05\n",
      "Epoch 30/50\n",
      "86/86 [==============================] - 15s 173ms/step - loss: 0.2849 - accuracy: 0.8939 - val_loss: 0.2435 - val_accuracy: 0.9152 - lr: 1.5474e-05\n",
      "Epoch 31/50\n",
      "86/86 [==============================] - 14s 169ms/step - loss: 0.2804 - accuracy: 0.8998 - val_loss: 0.2399 - val_accuracy: 0.9196 - lr: 1.2379e-05\n",
      "Epoch 32/50\n",
      "86/86 [==============================] - 15s 170ms/step - loss: 0.2769 - accuracy: 0.9044 - val_loss: 0.2374 - val_accuracy: 0.9196 - lr: 9.9035e-06\n",
      "Epoch 33/50\n",
      "86/86 [==============================] - 15s 169ms/step - loss: 0.2739 - accuracy: 0.9038 - val_loss: 0.2358 - val_accuracy: 0.9187 - lr: 7.9228e-06\n",
      "Epoch 34/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 0.2715 - accuracy: 0.9021 - val_loss: 0.2348 - val_accuracy: 0.9161 - lr: 6.3383e-06\n",
      "Epoch 35/50\n",
      "86/86 [==============================] - 15s 170ms/step - loss: 0.2695 - accuracy: 0.9009 - val_loss: 0.2341 - val_accuracy: 0.9161 - lr: 5.0706e-06\n",
      "Epoch 36/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 0.2678 - accuracy: 0.9021 - val_loss: 0.2336 - val_accuracy: 0.9152 - lr: 4.0565e-06\n",
      "Epoch 37/50\n",
      "86/86 [==============================] - 15s 170ms/step - loss: 0.2665 - accuracy: 0.9033 - val_loss: 0.2332 - val_accuracy: 0.9143 - lr: 3.2452e-06\n",
      "Epoch 38/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 0.2654 - accuracy: 0.9038 - val_loss: 0.2329 - val_accuracy: 0.9135 - lr: 2.5961e-06\n",
      "Epoch 39/50\n",
      "86/86 [==============================] - 15s 173ms/step - loss: 0.2645 - accuracy: 0.9038 - val_loss: 0.2327 - val_accuracy: 0.9143 - lr: 2.0769e-06\n",
      "Epoch 40/50\n",
      "86/86 [==============================] - 15s 169ms/step - loss: 0.2638 - accuracy: 0.9038 - val_loss: 0.2325 - val_accuracy: 0.9126 - lr: 1.6615e-06\n",
      "Epoch 41/50\n",
      "86/86 [==============================] - 15s 170ms/step - loss: 0.2633 - accuracy: 0.9038 - val_loss: 0.2323 - val_accuracy: 0.9117 - lr: 1.3292e-06\n",
      "Epoch 42/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 0.2628 - accuracy: 0.9044 - val_loss: 0.2322 - val_accuracy: 0.9117 - lr: 1.0634e-06\n",
      "Epoch 43/50\n",
      "86/86 [==============================] - 15s 170ms/step - loss: 0.2625 - accuracy: 0.9044 - val_loss: 0.2321 - val_accuracy: 0.9108 - lr: 8.5071e-07\n",
      "Epoch 44/50\n",
      "86/86 [==============================] - 15s 170ms/step - loss: 0.2622 - accuracy: 0.9056 - val_loss: 0.2320 - val_accuracy: 0.9108 - lr: 6.8056e-07\n",
      "Epoch 45/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 0.2619 - accuracy: 0.9050 - val_loss: 0.2320 - val_accuracy: 0.9100 - lr: 5.4445e-07\n",
      "Epoch 46/50\n",
      "86/86 [==============================] - 15s 169ms/step - loss: 0.2617 - accuracy: 0.9050 - val_loss: 0.2319 - val_accuracy: 0.9091 - lr: 4.3556e-07\n",
      "Epoch 47/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 0.2616 - accuracy: 0.9050 - val_loss: 0.2319 - val_accuracy: 0.9091 - lr: 3.4845e-07\n",
      "Epoch 48/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 0.2615 - accuracy: 0.9050 - val_loss: 0.2318 - val_accuracy: 0.9091 - lr: 2.7876e-07\n",
      "Epoch 49/50\n",
      "86/86 [==============================] - 15s 172ms/step - loss: 0.2614 - accuracy: 0.9050 - val_loss: 0.2318 - val_accuracy: 0.9091 - lr: 2.2301e-07\n",
      "Epoch 50/50\n",
      "86/86 [==============================] - 15s 171ms/step - loss: 0.2613 - accuracy: 0.9050 - val_loss: 0.2318 - val_accuracy: 0.9091 - lr: 1.7841e-07\n",
      "KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_13'), name='input_13', description=\"created by layer 'input_13'\")\n",
      "(None, 5)\n",
      "Epoch 1/50\n",
      "69/69 [==============================] - 21s 222ms/step - loss: 2.4024 - accuracy: 0.3386 - val_loss: 2476357376.0000 - val_accuracy: 0.1897 - lr: 0.0100\n",
      "Epoch 2/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 1.4179 - accuracy: 0.4009 - val_loss: 90951.2422 - val_accuracy: 0.1897 - lr: 0.0080\n",
      "Epoch 3/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 1.3665 - accuracy: 0.3864 - val_loss: 275.4962 - val_accuracy: 0.3217 - lr: 0.0064\n",
      "Epoch 4/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 1.1927 - accuracy: 0.4237 - val_loss: 5.3719 - val_accuracy: 0.3698 - lr: 0.0051\n",
      "Epoch 5/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 1.0931 - accuracy: 0.4645 - val_loss: 1.0948 - val_accuracy: 0.5175 - lr: 0.0041\n",
      "Epoch 6/50\n",
      "69/69 [==============================] - 14s 204ms/step - loss: 1.0157 - accuracy: 0.4965 - val_loss: 0.9155 - val_accuracy: 0.5629 - lr: 0.0033\n",
      "Epoch 7/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.8857 - accuracy: 0.5606 - val_loss: 0.7757 - val_accuracy: 0.5839 - lr: 0.0026\n",
      "Epoch 8/50\n",
      "69/69 [==============================] - 14s 200ms/step - loss: 0.7278 - accuracy: 0.6136 - val_loss: 1.0289 - val_accuracy: 0.6128 - lr: 0.0021\n",
      "Epoch 9/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.6377 - accuracy: 0.6638 - val_loss: 0.8077 - val_accuracy: 0.6434 - lr: 0.0017\n",
      "Epoch 10/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.5743 - accuracy: 0.6958 - val_loss: 0.8882 - val_accuracy: 0.6110 - lr: 0.0013\n",
      "Epoch 11/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.5206 - accuracy: 0.7413 - val_loss: 1.2259 - val_accuracy: 0.5944 - lr: 0.0011\n",
      "Epoch 12/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.4673 - accuracy: 0.7937 - val_loss: 3.1053 - val_accuracy: 0.3907 - lr: 8.5899e-04\n",
      "Epoch 13/50\n",
      "69/69 [==============================] - 14s 200ms/step - loss: 0.8192 - accuracy: 0.6550 - val_loss: 6.2374 - val_accuracy: 0.3252 - lr: 6.8719e-04\n",
      "Epoch 14/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.7798 - accuracy: 0.6789 - val_loss: 1.2102 - val_accuracy: 0.5262 - lr: 5.4976e-04\n",
      "Epoch 15/50\n",
      "69/69 [==============================] - 14s 202ms/step - loss: 0.7092 - accuracy: 0.6987 - val_loss: 1.0551 - val_accuracy: 0.5673 - lr: 4.3980e-04\n",
      "Epoch 16/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.6578 - accuracy: 0.7331 - val_loss: 1.8574 - val_accuracy: 0.3593 - lr: 3.5184e-04\n",
      "Epoch 17/50\n",
      "69/69 [==============================] - 14s 204ms/step - loss: 0.5271 - accuracy: 0.7931 - val_loss: 1.5260 - val_accuracy: 0.5245 - lr: 2.8147e-04\n",
      "Epoch 18/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.4356 - accuracy: 0.8246 - val_loss: 0.8765 - val_accuracy: 0.6469 - lr: 2.2518e-04\n",
      "Epoch 19/50\n",
      "69/69 [==============================] - 14s 205ms/step - loss: 0.3871 - accuracy: 0.8462 - val_loss: 0.6211 - val_accuracy: 0.7194 - lr: 1.8014e-04\n",
      "Epoch 20/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.3447 - accuracy: 0.8566 - val_loss: 0.6923 - val_accuracy: 0.7325 - lr: 1.4412e-04\n",
      "Epoch 21/50\n",
      "69/69 [==============================] - 14s 204ms/step - loss: 0.3075 - accuracy: 0.8788 - val_loss: 0.6857 - val_accuracy: 0.7509 - lr: 1.1529e-04\n",
      "Epoch 22/50\n",
      "69/69 [==============================] - 14s 204ms/step - loss: 0.2779 - accuracy: 0.8945 - val_loss: 0.5740 - val_accuracy: 0.8042 - lr: 9.2234e-05\n",
      "Epoch 23/50\n",
      "69/69 [==============================] - 14s 204ms/step - loss: 0.2568 - accuracy: 0.9044 - val_loss: 0.3974 - val_accuracy: 0.8514 - lr: 7.3787e-05\n",
      "Epoch 24/50\n",
      "69/69 [==============================] - 14s 203ms/step - loss: 0.2407 - accuracy: 0.9097 - val_loss: 0.2665 - val_accuracy: 0.9047 - lr: 5.9030e-05\n",
      "Epoch 25/50\n",
      "69/69 [==============================] - 14s 205ms/step - loss: 0.2245 - accuracy: 0.9155 - val_loss: 0.2067 - val_accuracy: 0.9257 - lr: 4.7224e-05\n",
      "Epoch 26/50\n",
      "69/69 [==============================] - 14s 203ms/step - loss: 0.2094 - accuracy: 0.9219 - val_loss: 0.1914 - val_accuracy: 0.9353 - lr: 3.7779e-05\n",
      "Epoch 27/50\n",
      "69/69 [==============================] - 14s 204ms/step - loss: 0.1968 - accuracy: 0.9318 - val_loss: 0.1851 - val_accuracy: 0.9327 - lr: 3.0223e-05\n",
      "Epoch 28/50\n",
      "69/69 [==============================] - 14s 203ms/step - loss: 0.1872 - accuracy: 0.9341 - val_loss: 0.1823 - val_accuracy: 0.9371 - lr: 2.4179e-05\n",
      "Epoch 29/50\n",
      "69/69 [==============================] - 14s 200ms/step - loss: 0.1778 - accuracy: 0.9400 - val_loss: 0.1716 - val_accuracy: 0.9414 - lr: 1.9343e-05\n",
      "Epoch 30/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.1721 - accuracy: 0.9429 - val_loss: 0.1849 - val_accuracy: 0.9344 - lr: 1.5474e-05\n",
      "Epoch 31/50\n",
      "69/69 [==============================] - 14s 200ms/step - loss: 0.1686 - accuracy: 0.9452 - val_loss: 0.2724 - val_accuracy: 0.9056 - lr: 1.2379e-05\n",
      "Epoch 32/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.1675 - accuracy: 0.9464 - val_loss: 0.1824 - val_accuracy: 0.9309 - lr: 9.9035e-06\n",
      "Epoch 33/50\n",
      "69/69 [==============================] - 14s 204ms/step - loss: 0.1614 - accuracy: 0.9470 - val_loss: 0.1657 - val_accuracy: 0.9441 - lr: 7.9228e-06\n",
      "Epoch 34/50\n",
      "69/69 [==============================] - 14s 202ms/step - loss: 0.1573 - accuracy: 0.9499 - val_loss: 0.1627 - val_accuracy: 0.9458 - lr: 6.3383e-06\n",
      "Epoch 35/50\n",
      "69/69 [==============================] - 14s 202ms/step - loss: 0.1549 - accuracy: 0.9499 - val_loss: 0.1621 - val_accuracy: 0.9476 - lr: 5.0706e-06\n",
      "Epoch 36/50\n",
      "69/69 [==============================] - 14s 204ms/step - loss: 0.1527 - accuracy: 0.9522 - val_loss: 0.1598 - val_accuracy: 0.9502 - lr: 4.0565e-06\n",
      "Epoch 37/50\n",
      "69/69 [==============================] - 14s 202ms/step - loss: 0.1514 - accuracy: 0.9528 - val_loss: 0.1605 - val_accuracy: 0.9458 - lr: 3.2452e-06\n",
      "Epoch 38/50\n",
      "69/69 [==============================] - 14s 204ms/step - loss: 0.1511 - accuracy: 0.9516 - val_loss: 0.1580 - val_accuracy: 0.9476 - lr: 2.5961e-06\n",
      "Epoch 39/50\n",
      "69/69 [==============================] - 14s 203ms/step - loss: 0.1503 - accuracy: 0.9522 - val_loss: 0.1575 - val_accuracy: 0.9476 - lr: 2.0769e-06\n",
      "Epoch 40/50\n",
      "69/69 [==============================] - 14s 203ms/step - loss: 0.1494 - accuracy: 0.9516 - val_loss: 0.1564 - val_accuracy: 0.9484 - lr: 1.6615e-06\n",
      "Epoch 41/50\n",
      "69/69 [==============================] - 14s 203ms/step - loss: 0.1487 - accuracy: 0.9522 - val_loss: 0.1556 - val_accuracy: 0.9484 - lr: 1.3292e-06\n",
      "Epoch 42/50\n",
      "69/69 [==============================] - 14s 205ms/step - loss: 0.1480 - accuracy: 0.9540 - val_loss: 0.1552 - val_accuracy: 0.9493 - lr: 1.0634e-06\n",
      "Epoch 43/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.1475 - accuracy: 0.9545 - val_loss: 0.1550 - val_accuracy: 0.9493 - lr: 8.5071e-07\n",
      "Epoch 44/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.1471 - accuracy: 0.9545 - val_loss: 0.1548 - val_accuracy: 0.9493 - lr: 6.8056e-07\n",
      "Epoch 45/50\n",
      "69/69 [==============================] - 14s 202ms/step - loss: 0.1468 - accuracy: 0.9545 - val_loss: 0.1547 - val_accuracy: 0.9493 - lr: 5.4445e-07\n",
      "Epoch 46/50\n",
      "69/69 [==============================] - 14s 204ms/step - loss: 0.1466 - accuracy: 0.9545 - val_loss: 0.1546 - val_accuracy: 0.9493 - lr: 4.3556e-07\n",
      "Epoch 47/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.1464 - accuracy: 0.9545 - val_loss: 0.1545 - val_accuracy: 0.9493 - lr: 3.4845e-07\n",
      "Epoch 48/50\n",
      "69/69 [==============================] - 14s 208ms/step - loss: 0.1462 - accuracy: 0.9545 - val_loss: 0.1544 - val_accuracy: 0.9493 - lr: 2.7876e-07\n",
      "Epoch 49/50\n",
      "69/69 [==============================] - 14s 201ms/step - loss: 0.1461 - accuracy: 0.9545 - val_loss: 0.1543 - val_accuracy: 0.9493 - lr: 2.2301e-07\n",
      "Epoch 50/50\n",
      "69/69 [==============================] - 15s 212ms/step - loss: 0.1460 - accuracy: 0.9545 - val_loss: 0.1543 - val_accuracy: 0.9493 - lr: 1.7841e-07\n",
      "KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_14'), name='input_14', description=\"created by layer 'input_14'\")\n",
      "(None, 5)\n",
      "Epoch 1/50\n",
      "58/58 [==============================] - 23s 282ms/step - loss: 3.8936 - accuracy: 0.3036 - val_loss: 20799318016.0000 - val_accuracy: 0.2028 - lr: 0.0100\n",
      "Epoch 2/50\n",
      "58/58 [==============================] - 13s 227ms/step - loss: 2.0656 - accuracy: 0.3543 - val_loss: 29165.3594 - val_accuracy: 0.2010 - lr: 0.0080\n",
      "Epoch 3/50\n",
      "58/58 [==============================] - 13s 228ms/step - loss: 1.5210 - accuracy: 0.4003 - val_loss: 21.6415 - val_accuracy: 0.2369 - lr: 0.0064\n",
      "Epoch 4/50\n",
      "58/58 [==============================] - 13s 227ms/step - loss: 1.2729 - accuracy: 0.4091 - val_loss: 4.8914 - val_accuracy: 0.3610 - lr: 0.0051\n",
      "Epoch 5/50\n",
      "58/58 [==============================] - 13s 229ms/step - loss: 1.2741 - accuracy: 0.3887 - val_loss: 9.6171 - val_accuracy: 0.3462 - lr: 0.0041\n",
      "Epoch 6/50\n",
      "58/58 [==============================] - 13s 231ms/step - loss: 1.2116 - accuracy: 0.4545 - val_loss: 2.0065 - val_accuracy: 0.2561 - lr: 0.0033\n",
      "Epoch 7/50\n",
      "58/58 [==============================] - 13s 230ms/step - loss: 1.1706 - accuracy: 0.4685 - val_loss: 4.2232 - val_accuracy: 0.1879 - lr: 0.0026\n",
      "Epoch 8/50\n",
      "58/58 [==============================] - 13s 230ms/step - loss: 1.1304 - accuracy: 0.4493 - val_loss: 2.5169 - val_accuracy: 0.1906 - lr: 0.0021\n",
      "Epoch 9/50\n",
      "58/58 [==============================] - 13s 227ms/step - loss: 1.1279 - accuracy: 0.4755 - val_loss: 9.0166 - val_accuracy: 0.1451 - lr: 0.0017\n",
      "Epoch 10/50\n",
      "58/58 [==============================] - 13s 231ms/step - loss: 1.0168 - accuracy: 0.5198 - val_loss: 3.2621 - val_accuracy: 0.3986 - lr: 0.0013\n",
      "Epoch 11/50\n",
      "58/58 [==============================] - 13s 232ms/step - loss: 1.0228 - accuracy: 0.5280 - val_loss: 2.5708 - val_accuracy: 0.3916 - lr: 0.0011\n",
      "Epoch 12/50\n",
      "58/58 [==============================] - 13s 227ms/step - loss: 0.9615 - accuracy: 0.5513 - val_loss: 27.7122 - val_accuracy: 0.3802 - lr: 8.5899e-04\n",
      "Epoch 13/50\n",
      "58/58 [==============================] - 15s 265ms/step - loss: 0.8651 - accuracy: 0.5816 - val_loss: 2.7907 - val_accuracy: 0.3514 - lr: 6.8719e-04\n",
      "Epoch 14/50\n",
      "58/58 [==============================] - 13s 227ms/step - loss: 0.8033 - accuracy: 0.6061 - val_loss: 1.6404 - val_accuracy: 0.4030 - lr: 5.4976e-04\n",
      "Epoch 15/50\n",
      "58/58 [==============================] - 17s 296ms/step - loss: 0.7525 - accuracy: 0.6311 - val_loss: 2.2763 - val_accuracy: 0.3068 - lr: 4.3980e-04\n",
      "Epoch 16/50\n",
      "58/58 [==============================] - 19s 333ms/step - loss: 0.7062 - accuracy: 0.6480 - val_loss: 1.1586 - val_accuracy: 0.5157 - lr: 3.5184e-04\n",
      "Epoch 17/50\n",
      "58/58 [==============================] - 21s 368ms/step - loss: 0.6726 - accuracy: 0.6649 - val_loss: 1.7561 - val_accuracy: 0.4047 - lr: 2.8147e-04\n",
      "Epoch 18/50\n",
      "58/58 [==============================] - 13s 227ms/step - loss: 0.6566 - accuracy: 0.6777 - val_loss: 0.6744 - val_accuracy: 0.6748 - lr: 2.2518e-04\n",
      "Epoch 19/50\n",
      "58/58 [==============================] - 13s 232ms/step - loss: 0.6309 - accuracy: 0.6923 - val_loss: 1.2258 - val_accuracy: 0.4965 - lr: 1.8014e-04\n",
      "Epoch 20/50\n",
      "58/58 [==============================] - 13s 231ms/step - loss: 0.6284 - accuracy: 0.6952 - val_loss: 1.2922 - val_accuracy: 0.4843 - lr: 1.4412e-04\n",
      "Epoch 21/50\n",
      "58/58 [==============================] - 13s 230ms/step - loss: 0.6057 - accuracy: 0.7051 - val_loss: 0.7025 - val_accuracy: 0.6582 - lr: 1.1529e-04\n",
      "Epoch 22/50\n",
      "58/58 [==============================] - 13s 231ms/step - loss: 0.6023 - accuracy: 0.7075 - val_loss: 0.5394 - val_accuracy: 0.7421 - lr: 9.2234e-05\n",
      "Epoch 23/50\n",
      "58/58 [==============================] - 13s 230ms/step - loss: 0.5921 - accuracy: 0.7104 - val_loss: 0.7294 - val_accuracy: 0.6538 - lr: 7.3787e-05\n",
      "Epoch 24/50\n",
      "58/58 [==============================] - 18s 306ms/step - loss: 0.5854 - accuracy: 0.7244 - val_loss: 0.5502 - val_accuracy: 0.7299 - lr: 5.9030e-05\n",
      "Epoch 25/50\n",
      "58/58 [==============================] - 20s 355ms/step - loss: 0.5839 - accuracy: 0.7191 - val_loss: 0.6564 - val_accuracy: 0.6888 - lr: 4.7224e-05\n",
      "Epoch 26/50\n",
      "58/58 [==============================] - 20s 349ms/step - loss: 0.5729 - accuracy: 0.7267 - val_loss: 0.5410 - val_accuracy: 0.7334 - lr: 3.7779e-05\n",
      "Epoch 27/50\n",
      "58/58 [==============================] - 19s 323ms/step - loss: 0.5686 - accuracy: 0.7214 - val_loss: 0.5790 - val_accuracy: 0.7194 - lr: 3.0223e-05\n",
      "Epoch 28/50\n",
      "58/58 [==============================] - 14s 249ms/step - loss: 0.5679 - accuracy: 0.7267 - val_loss: 0.5217 - val_accuracy: 0.7404 - lr: 2.4179e-05\n",
      "Epoch 29/50\n",
      "58/58 [==============================] - 13s 231ms/step - loss: 0.5610 - accuracy: 0.7290 - val_loss: 0.5305 - val_accuracy: 0.7325 - lr: 1.9343e-05\n",
      "Epoch 30/50\n",
      "58/58 [==============================] - 13s 233ms/step - loss: 0.5591 - accuracy: 0.7314 - val_loss: 0.5164 - val_accuracy: 0.7509 - lr: 1.5474e-05\n",
      "Epoch 31/50\n",
      "58/58 [==============================] - 21s 368ms/step - loss: 0.5558 - accuracy: 0.7296 - val_loss: 0.5149 - val_accuracy: 0.7448 - lr: 1.2379e-05\n",
      "Epoch 32/50\n",
      "58/58 [==============================] - 19s 328ms/step - loss: 0.5542 - accuracy: 0.7319 - val_loss: 0.5118 - val_accuracy: 0.7500 - lr: 9.9035e-06\n",
      "Epoch 33/50\n",
      "58/58 [==============================] - 18s 317ms/step - loss: 0.5530 - accuracy: 0.7337 - val_loss: 0.5098 - val_accuracy: 0.7517 - lr: 7.9228e-06\n",
      "Epoch 34/50\n",
      "58/58 [==============================] - 19s 330ms/step - loss: 0.5520 - accuracy: 0.7360 - val_loss: 0.5085 - val_accuracy: 0.7517 - lr: 6.3383e-06\n",
      "Epoch 35/50\n",
      "58/58 [==============================] - 13s 228ms/step - loss: 0.5512 - accuracy: 0.7372 - val_loss: 0.5076 - val_accuracy: 0.7509 - lr: 5.0706e-06\n",
      "Epoch 36/50\n",
      "58/58 [==============================] - 13s 227ms/step - loss: 0.5506 - accuracy: 0.7360 - val_loss: 0.5069 - val_accuracy: 0.7500 - lr: 4.0565e-06\n",
      "Epoch 37/50\n",
      "58/58 [==============================] - 13s 229ms/step - loss: 0.5500 - accuracy: 0.7372 - val_loss: 0.5063 - val_accuracy: 0.7500 - lr: 3.2452e-06\n",
      "Epoch 38/50\n",
      "58/58 [==============================] - 13s 227ms/step - loss: 0.5496 - accuracy: 0.7378 - val_loss: 0.5059 - val_accuracy: 0.7500 - lr: 2.5961e-06\n",
      "Epoch 39/50\n",
      "58/58 [==============================] - 13s 230ms/step - loss: 0.5493 - accuracy: 0.7378 - val_loss: 0.5055 - val_accuracy: 0.7500 - lr: 2.0769e-06\n",
      "Epoch 40/50\n",
      "58/58 [==============================] - 13s 229ms/step - loss: 0.5490 - accuracy: 0.7383 - val_loss: 0.5052 - val_accuracy: 0.7509 - lr: 1.6615e-06\n",
      "Epoch 41/50\n",
      "58/58 [==============================] - 13s 231ms/step - loss: 0.5488 - accuracy: 0.7383 - val_loss: 0.5050 - val_accuracy: 0.7517 - lr: 1.3292e-06\n",
      "Epoch 42/50\n",
      "58/58 [==============================] - 13s 228ms/step - loss: 0.5486 - accuracy: 0.7389 - val_loss: 0.5048 - val_accuracy: 0.7517 - lr: 1.0634e-06\n",
      "Epoch 43/50\n",
      "58/58 [==============================] - 13s 228ms/step - loss: 0.5485 - accuracy: 0.7389 - val_loss: 0.5047 - val_accuracy: 0.7517 - lr: 8.5071e-07\n",
      "Epoch 44/50\n",
      "58/58 [==============================] - 13s 231ms/step - loss: 0.5484 - accuracy: 0.7383 - val_loss: 0.5046 - val_accuracy: 0.7517 - lr: 6.8056e-07\n",
      "Epoch 45/50\n",
      "58/58 [==============================] - 13s 229ms/step - loss: 0.5483 - accuracy: 0.7389 - val_loss: 0.5045 - val_accuracy: 0.7517 - lr: 5.4445e-07\n",
      "Epoch 46/50\n",
      "58/58 [==============================] - 13s 230ms/step - loss: 0.5482 - accuracy: 0.7389 - val_loss: 0.5044 - val_accuracy: 0.7517 - lr: 4.3556e-07\n",
      "Epoch 47/50\n",
      "58/58 [==============================] - 13s 226ms/step - loss: 0.5481 - accuracy: 0.7383 - val_loss: 0.5044 - val_accuracy: 0.7517 - lr: 3.4845e-07\n",
      "Epoch 48/50\n",
      "58/58 [==============================] - 13s 228ms/step - loss: 0.5481 - accuracy: 0.7389 - val_loss: 0.5043 - val_accuracy: 0.7517 - lr: 2.7876e-07\n",
      "Epoch 49/50\n",
      "58/58 [==============================] - 13s 227ms/step - loss: 0.5480 - accuracy: 0.7389 - val_loss: 0.5043 - val_accuracy: 0.7517 - lr: 2.2301e-07\n",
      "Epoch 50/50\n",
      "58/58 [==============================] - 13s 231ms/step - loss: 0.5480 - accuracy: 0.7389 - val_loss: 0.5043 - val_accuracy: 0.7517 - lr: 1.7841e-07\n",
      "KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_15'), name='input_15', description=\"created by layer 'input_15'\")\n",
      "(None, 5)\n",
      "Epoch 1/50\n",
      "50/50 [==============================] - 20s 286ms/step - loss: 3.5297 - accuracy: 0.3537 - val_loss: 168605532160.0000 - val_accuracy: 0.1932 - lr: 0.0100\n",
      "Epoch 2/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 2.2089 - accuracy: 0.3939 - val_loss: 112055.0938 - val_accuracy: 0.1932 - lr: 0.0080\n",
      "Epoch 3/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 1.3258 - accuracy: 0.4528 - val_loss: 2.3347 - val_accuracy: 0.2736 - lr: 0.0064\n",
      "Epoch 4/50\n",
      "50/50 [==============================] - 13s 260ms/step - loss: 1.2679 - accuracy: 0.4650 - val_loss: 4.5752 - val_accuracy: 0.3899 - lr: 0.0051\n",
      "Epoch 5/50\n",
      "50/50 [==============================] - 13s 263ms/step - loss: 0.8522 - accuracy: 0.5828 - val_loss: 2.8326 - val_accuracy: 0.2684 - lr: 0.0041\n",
      "Epoch 6/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.8537 - accuracy: 0.6078 - val_loss: 4.0005 - val_accuracy: 0.3094 - lr: 0.0033\n",
      "Epoch 7/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 1.2217 - accuracy: 0.5268 - val_loss: 2.5442 - val_accuracy: 0.3051 - lr: 0.0026\n",
      "Epoch 8/50\n",
      "50/50 [==============================] - 13s 259ms/step - loss: 1.0618 - accuracy: 0.5239 - val_loss: 2.4236 - val_accuracy: 0.3295 - lr: 0.0021\n",
      "Epoch 9/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.7422 - accuracy: 0.6334 - val_loss: 6.2436 - val_accuracy: 0.3051 - lr: 0.0017\n",
      "Epoch 10/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.7275 - accuracy: 0.6381 - val_loss: 2.8887 - val_accuracy: 0.1687 - lr: 0.0013\n",
      "Epoch 11/50\n",
      "50/50 [==============================] - 13s 260ms/step - loss: 0.7305 - accuracy: 0.6428 - val_loss: 4.2743 - val_accuracy: 0.1853 - lr: 0.0011\n",
      "Epoch 12/50\n",
      "50/50 [==============================] - 13s 263ms/step - loss: 0.6544 - accuracy: 0.6643 - val_loss: 2.4063 - val_accuracy: 0.4065 - lr: 8.5899e-04\n",
      "Epoch 13/50\n",
      "50/50 [==============================] - 13s 259ms/step - loss: 0.6488 - accuracy: 0.6789 - val_loss: 2.9973 - val_accuracy: 0.2867 - lr: 6.8719e-04\n",
      "Epoch 14/50\n",
      "50/50 [==============================] - 13s 264ms/step - loss: 0.6192 - accuracy: 0.6888 - val_loss: 2.2575 - val_accuracy: 0.3785 - lr: 5.4976e-04\n",
      "Epoch 15/50\n",
      "50/50 [==============================] - 13s 261ms/step - loss: 0.5884 - accuracy: 0.7022 - val_loss: 1.8247 - val_accuracy: 0.4965 - lr: 4.3980e-04\n",
      "Epoch 16/50\n",
      "50/50 [==============================] - 13s 262ms/step - loss: 0.5541 - accuracy: 0.7267 - val_loss: 1.6949 - val_accuracy: 0.5052 - lr: 3.5184e-04\n",
      "Epoch 17/50\n",
      "50/50 [==============================] - 13s 260ms/step - loss: 0.5369 - accuracy: 0.7343 - val_loss: 1.2922 - val_accuracy: 0.5699 - lr: 2.8147e-04\n",
      "Epoch 18/50\n",
      "50/50 [==============================] - 13s 259ms/step - loss: 0.5319 - accuracy: 0.7436 - val_loss: 0.8453 - val_accuracy: 0.6337 - lr: 2.2518e-04\n",
      "Epoch 19/50\n",
      "50/50 [==============================] - 13s 263ms/step - loss: 0.5241 - accuracy: 0.7418 - val_loss: 0.6556 - val_accuracy: 0.6503 - lr: 1.8014e-04\n",
      "Epoch 20/50\n",
      "50/50 [==============================] - 13s 260ms/step - loss: 0.5104 - accuracy: 0.7576 - val_loss: 0.5587 - val_accuracy: 0.7247 - lr: 1.4412e-04\n",
      "Epoch 21/50\n",
      "50/50 [==============================] - 13s 259ms/step - loss: 0.4872 - accuracy: 0.7669 - val_loss: 0.5440 - val_accuracy: 0.7264 - lr: 1.1529e-04\n",
      "Epoch 22/50\n",
      "50/50 [==============================] - 13s 259ms/step - loss: 0.4753 - accuracy: 0.7797 - val_loss: 0.5099 - val_accuracy: 0.7238 - lr: 9.2234e-05\n",
      "Epoch 23/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4684 - accuracy: 0.7797 - val_loss: 0.4881 - val_accuracy: 0.7290 - lr: 7.3787e-05\n",
      "Epoch 24/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4617 - accuracy: 0.7861 - val_loss: 0.5545 - val_accuracy: 0.6879 - lr: 5.9030e-05\n",
      "Epoch 25/50\n",
      "50/50 [==============================] - 13s 263ms/step - loss: 0.4553 - accuracy: 0.7885 - val_loss: 0.4576 - val_accuracy: 0.7491 - lr: 4.7224e-05\n",
      "Epoch 26/50\n",
      "50/50 [==============================] - 13s 264ms/step - loss: 0.4502 - accuracy: 0.7931 - val_loss: 0.4417 - val_accuracy: 0.7666 - lr: 3.7779e-05\n",
      "Epoch 27/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4462 - accuracy: 0.7955 - val_loss: 0.4420 - val_accuracy: 0.7622 - lr: 3.0223e-05\n",
      "Epoch 28/50\n",
      "50/50 [==============================] - 13s 259ms/step - loss: 0.4430 - accuracy: 0.7972 - val_loss: 0.4379 - val_accuracy: 0.7614 - lr: 2.4179e-05\n",
      "Epoch 29/50\n",
      "50/50 [==============================] - 13s 265ms/step - loss: 0.4401 - accuracy: 0.7978 - val_loss: 0.4383 - val_accuracy: 0.7640 - lr: 1.9343e-05\n",
      "Epoch 30/50\n",
      "50/50 [==============================] - 13s 261ms/step - loss: 0.4377 - accuracy: 0.7978 - val_loss: 0.4428 - val_accuracy: 0.7614 - lr: 1.5474e-05\n",
      "Epoch 31/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4359 - accuracy: 0.7984 - val_loss: 0.4401 - val_accuracy: 0.7657 - lr: 1.2379e-05\n",
      "Epoch 32/50\n",
      "50/50 [==============================] - 13s 259ms/step - loss: 0.4344 - accuracy: 0.7995 - val_loss: 0.4409 - val_accuracy: 0.7631 - lr: 9.9035e-06\n",
      "Epoch 33/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4332 - accuracy: 0.7995 - val_loss: 0.4389 - val_accuracy: 0.7675 - lr: 7.9228e-06\n",
      "Epoch 34/50\n",
      "50/50 [==============================] - 13s 261ms/step - loss: 0.4322 - accuracy: 0.8001 - val_loss: 0.4374 - val_accuracy: 0.7675 - lr: 6.3383e-06\n",
      "Epoch 35/50\n",
      "50/50 [==============================] - 13s 262ms/step - loss: 0.4314 - accuracy: 0.8007 - val_loss: 0.4372 - val_accuracy: 0.7666 - lr: 5.0706e-06\n",
      "Epoch 36/50\n",
      "50/50 [==============================] - 13s 259ms/step - loss: 0.4308 - accuracy: 0.8007 - val_loss: 0.4375 - val_accuracy: 0.7649 - lr: 4.0565e-06\n",
      "Epoch 37/50\n",
      "50/50 [==============================] - 13s 260ms/step - loss: 0.4302 - accuracy: 0.8019 - val_loss: 0.4381 - val_accuracy: 0.7666 - lr: 3.2452e-06\n",
      "Epoch 38/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4298 - accuracy: 0.8036 - val_loss: 0.4373 - val_accuracy: 0.7675 - lr: 2.5961e-06\n",
      "Epoch 39/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4295 - accuracy: 0.8042 - val_loss: 0.4372 - val_accuracy: 0.7684 - lr: 2.0769e-06\n",
      "Epoch 40/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4292 - accuracy: 0.8042 - val_loss: 0.4373 - val_accuracy: 0.7675 - lr: 1.6615e-06\n",
      "Epoch 41/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4290 - accuracy: 0.8042 - val_loss: 0.4377 - val_accuracy: 0.7675 - lr: 1.3292e-06\n",
      "Epoch 42/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4288 - accuracy: 0.8042 - val_loss: 0.4377 - val_accuracy: 0.7684 - lr: 1.0634e-06\n",
      "Epoch 43/50\n",
      "50/50 [==============================] - 13s 263ms/step - loss: 0.4287 - accuracy: 0.8042 - val_loss: 0.4378 - val_accuracy: 0.7666 - lr: 8.5071e-07\n",
      "Epoch 44/50\n",
      "50/50 [==============================] - 13s 261ms/step - loss: 0.4285 - accuracy: 0.8042 - val_loss: 0.4378 - val_accuracy: 0.7675 - lr: 6.8056e-07\n",
      "Epoch 45/50\n",
      "50/50 [==============================] - 13s 259ms/step - loss: 0.4284 - accuracy: 0.8042 - val_loss: 0.4379 - val_accuracy: 0.7666 - lr: 5.4445e-07\n",
      "Epoch 46/50\n",
      "50/50 [==============================] - 13s 263ms/step - loss: 0.4284 - accuracy: 0.8042 - val_loss: 0.4378 - val_accuracy: 0.7666 - lr: 4.3556e-07\n",
      "Epoch 47/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4283 - accuracy: 0.8042 - val_loss: 0.4379 - val_accuracy: 0.7675 - lr: 3.4845e-07\n",
      "Epoch 48/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4283 - accuracy: 0.8042 - val_loss: 0.4379 - val_accuracy: 0.7675 - lr: 2.7876e-07\n",
      "Epoch 49/50\n",
      "50/50 [==============================] - 13s 264ms/step - loss: 0.4282 - accuracy: 0.8042 - val_loss: 0.4379 - val_accuracy: 0.7675 - lr: 2.2301e-07\n",
      "Epoch 50/50\n",
      "50/50 [==============================] - 13s 258ms/step - loss: 0.4282 - accuracy: 0.8042 - val_loss: 0.4379 - val_accuracy: 0.7666 - lr: 1.7841e-07\n",
      "KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_16'), name='input_16', description=\"created by layer 'input_16'\")\n",
      "(None, 5)\n",
      "Epoch 1/50\n",
      "43/43 [==============================] - 19s 315ms/step - loss: 3.2478 - accuracy: 0.3263 - val_loss: 1055388270592.0000 - val_accuracy: 0.1897 - lr: 0.0100\n",
      "Epoch 2/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 1.2788 - accuracy: 0.4819 - val_loss: 452765376.0000 - val_accuracy: 0.1897 - lr: 0.0080\n",
      "Epoch 3/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.9930 - accuracy: 0.5507 - val_loss: 346408.1250 - val_accuracy: 0.1897 - lr: 0.0064\n",
      "Epoch 4/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.7723 - accuracy: 0.6579 - val_loss: 16949.3301 - val_accuracy: 0.1897 - lr: 0.0051\n",
      "Epoch 5/50\n",
      "43/43 [==============================] - 13s 295ms/step - loss: 0.7249 - accuracy: 0.7197 - val_loss: 11.8171 - val_accuracy: 0.3907 - lr: 0.0041\n",
      "Epoch 6/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.6289 - accuracy: 0.7383 - val_loss: 2.4590 - val_accuracy: 0.4528 - lr: 0.0033\n",
      "Epoch 7/50\n",
      "43/43 [==============================] - 12s 292ms/step - loss: 0.5665 - accuracy: 0.7902 - val_loss: 1.1903 - val_accuracy: 0.6836 - lr: 0.0026\n",
      "Epoch 8/50\n",
      "43/43 [==============================] - 13s 295ms/step - loss: 0.4347 - accuracy: 0.8193 - val_loss: 1.3761 - val_accuracy: 0.5944 - lr: 0.0021\n",
      "Epoch 9/50\n",
      "43/43 [==============================] - 13s 294ms/step - loss: 0.3716 - accuracy: 0.8718 - val_loss: 0.8555 - val_accuracy: 0.6407 - lr: 0.0017\n",
      "Epoch 10/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.2886 - accuracy: 0.8980 - val_loss: 1.5996 - val_accuracy: 0.5883 - lr: 0.0013\n",
      "Epoch 11/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 0.2459 - accuracy: 0.9143 - val_loss: 1.1271 - val_accuracy: 0.6731 - lr: 0.0011\n",
      "Epoch 12/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.2257 - accuracy: 0.9149 - val_loss: 0.8769 - val_accuracy: 0.7378 - lr: 8.5899e-04\n",
      "Epoch 13/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 0.1975 - accuracy: 0.9277 - val_loss: 0.6497 - val_accuracy: 0.7920 - lr: 6.8719e-04\n",
      "Epoch 14/50\n",
      "43/43 [==============================] - 13s 293ms/step - loss: 0.1697 - accuracy: 0.9429 - val_loss: 0.3913 - val_accuracy: 0.8374 - lr: 5.4976e-04\n",
      "Epoch 15/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 0.1487 - accuracy: 0.9487 - val_loss: 0.2230 - val_accuracy: 0.9231 - lr: 4.3980e-04\n",
      "Epoch 16/50\n",
      "43/43 [==============================] - 13s 293ms/step - loss: 0.1283 - accuracy: 0.9592 - val_loss: 0.1512 - val_accuracy: 0.9484 - lr: 3.5184e-04\n",
      "Epoch 17/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0974 - accuracy: 0.9726 - val_loss: 0.1425 - val_accuracy: 0.9458 - lr: 2.8147e-04\n",
      "Epoch 18/50\n",
      "43/43 [==============================] - 13s 295ms/step - loss: 0.0864 - accuracy: 0.9749 - val_loss: 0.1231 - val_accuracy: 0.9580 - lr: 2.2518e-04\n",
      "Epoch 19/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0788 - accuracy: 0.9755 - val_loss: 0.1154 - val_accuracy: 0.9598 - lr: 1.8014e-04\n",
      "Epoch 20/50\n",
      "43/43 [==============================] - 13s 294ms/step - loss: 0.0728 - accuracy: 0.9790 - val_loss: 0.1192 - val_accuracy: 0.9615 - lr: 1.4412e-04\n",
      "Epoch 21/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0683 - accuracy: 0.9790 - val_loss: 0.1260 - val_accuracy: 0.9563 - lr: 1.1529e-04\n",
      "Epoch 22/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0645 - accuracy: 0.9808 - val_loss: 0.1178 - val_accuracy: 0.9589 - lr: 9.2234e-05\n",
      "Epoch 23/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0615 - accuracy: 0.9802 - val_loss: 0.0943 - val_accuracy: 0.9659 - lr: 7.3787e-05\n",
      "Epoch 24/50\n",
      "43/43 [==============================] - 13s 293ms/step - loss: 0.0579 - accuracy: 0.9831 - val_loss: 0.0778 - val_accuracy: 0.9694 - lr: 5.9030e-05\n",
      "Epoch 25/50\n",
      "43/43 [==============================] - 12s 292ms/step - loss: 0.0547 - accuracy: 0.9843 - val_loss: 0.0719 - val_accuracy: 0.9747 - lr: 4.7224e-05\n",
      "Epoch 26/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 0.0524 - accuracy: 0.9866 - val_loss: 0.0689 - val_accuracy: 0.9764 - lr: 3.7779e-05\n",
      "Epoch 27/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0508 - accuracy: 0.9878 - val_loss: 0.0670 - val_accuracy: 0.9790 - lr: 3.0223e-05\n",
      "Epoch 28/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0496 - accuracy: 0.9883 - val_loss: 0.0658 - val_accuracy: 0.9790 - lr: 2.4179e-05\n",
      "Epoch 29/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 0.0487 - accuracy: 0.9895 - val_loss: 0.0649 - val_accuracy: 0.9790 - lr: 1.9343e-05\n",
      "Epoch 30/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0479 - accuracy: 0.9895 - val_loss: 0.0643 - val_accuracy: 0.9790 - lr: 1.5474e-05\n",
      "Epoch 31/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0473 - accuracy: 0.9895 - val_loss: 0.0639 - val_accuracy: 0.9790 - lr: 1.2379e-05\n",
      "Epoch 32/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0468 - accuracy: 0.9895 - val_loss: 0.0636 - val_accuracy: 0.9781 - lr: 9.9035e-06\n",
      "Epoch 33/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 0.0464 - accuracy: 0.9895 - val_loss: 0.0634 - val_accuracy: 0.9781 - lr: 7.9228e-06\n",
      "Epoch 34/50\n",
      "43/43 [==============================] - 13s 293ms/step - loss: 0.0461 - accuracy: 0.9895 - val_loss: 0.0633 - val_accuracy: 0.9781 - lr: 6.3383e-06\n",
      "Epoch 35/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0458 - accuracy: 0.9895 - val_loss: 0.0631 - val_accuracy: 0.9781 - lr: 5.0706e-06\n",
      "Epoch 36/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0456 - accuracy: 0.9895 - val_loss: 0.0630 - val_accuracy: 0.9781 - lr: 4.0565e-06\n",
      "Epoch 37/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0454 - accuracy: 0.9901 - val_loss: 0.0630 - val_accuracy: 0.9790 - lr: 3.2452e-06\n",
      "Epoch 38/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0453 - accuracy: 0.9901 - val_loss: 0.0629 - val_accuracy: 0.9790 - lr: 2.5961e-06\n",
      "Epoch 39/50\n",
      "43/43 [==============================] - 13s 293ms/step - loss: 0.0452 - accuracy: 0.9901 - val_loss: 0.0629 - val_accuracy: 0.9790 - lr: 2.0769e-06\n",
      "Epoch 40/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0451 - accuracy: 0.9901 - val_loss: 0.0628 - val_accuracy: 0.9790 - lr: 1.6615e-06\n",
      "Epoch 41/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0450 - accuracy: 0.9901 - val_loss: 0.0628 - val_accuracy: 0.9790 - lr: 1.3292e-06\n",
      "Epoch 42/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0450 - accuracy: 0.9901 - val_loss: 0.0628 - val_accuracy: 0.9790 - lr: 1.0634e-06\n",
      "Epoch 43/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0449 - accuracy: 0.9901 - val_loss: 0.0628 - val_accuracy: 0.9799 - lr: 8.5071e-07\n",
      "Epoch 44/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 0.0449 - accuracy: 0.9901 - val_loss: 0.0628 - val_accuracy: 0.9799 - lr: 6.8056e-07\n",
      "Epoch 45/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0448 - accuracy: 0.9901 - val_loss: 0.0627 - val_accuracy: 0.9799 - lr: 5.4445e-07\n",
      "Epoch 46/50\n",
      "43/43 [==============================] - 12s 291ms/step - loss: 0.0448 - accuracy: 0.9901 - val_loss: 0.0627 - val_accuracy: 0.9799 - lr: 4.3556e-07\n",
      "Epoch 47/50\n",
      "43/43 [==============================] - 14s 321ms/step - loss: 0.0448 - accuracy: 0.9901 - val_loss: 0.0627 - val_accuracy: 0.9799 - lr: 3.4845e-07\n",
      "Epoch 48/50\n",
      "43/43 [==============================] - 16s 374ms/step - loss: 0.0448 - accuracy: 0.9901 - val_loss: 0.0627 - val_accuracy: 0.9799 - lr: 2.7876e-07\n",
      "Epoch 49/50\n",
      "43/43 [==============================] - 18s 420ms/step - loss: 0.0447 - accuracy: 0.9901 - val_loss: 0.0627 - val_accuracy: 0.9799 - lr: 2.2301e-07\n",
      "Epoch 50/50\n",
      "43/43 [==============================] - 19s 433ms/step - loss: 0.0447 - accuracy: 0.9901 - val_loss: 0.0627 - val_accuracy: 0.9799 - lr: 1.7841e-07\n"
     ]
    }
   ],
   "source": [
    "batch_models = []\n",
    "for batch in differ_batch:\n",
    "    resnet_model = ResNet50(input_shape=(224, 224, 3), classes=5)\n",
    "    resnet_model.compile(optimizer='adam', # 优化器\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), # 损失函数\n",
    "              metrics=['accuracy']) # 指标\n",
    "    # 训练数据导入\n",
    "    History = resnet_model.fit(train_image_label_ds.batch(batch), epochs=50,validation_data=test_image_label_ds.batch(batch),callbacks=[tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-2 * (0.8 ** epoch))])\n",
    "    # 学习率训练, epochs=5代表训练5次\n",
    "    batch_models.append(History.history)\n",
    "# for lr in differ_lr:\n",
    "#     optimizer = tf.keras.optimizers.Adam(lr)\n",
    "#     resnet_model = ResNet50(input_shape=(224, 224, 3), classes=5)\n",
    "#     resnet_model.compile(optimizer=optimizer, # 优化器\n",
    "#               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), # 损失函数\n",
    "#               metrics=['accuracy']) # 指标\n",
    "#     # 训练数据导入\n",
    "#     History = resnet_model.fit(train_image_label_ds.batch(40), epochs=50,validation_data=test_image_label_ds.batch(40),callbacks=[tf.keras.callbacks.LearningRateScheduler(lambda epoch: lr * (0.9 ** epoch))])\n",
    "#     # 学习率训练, epochs=5代表训练5次\n",
    "#     lr_models.append(History.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# accuracy的历史\n",
    "fig,ax = plt.subplots(figsize=(16,12),nrows=2,ncols=1)\n",
    "for index, i in enumerate(batch_models):\n",
    "    # plt.plot(i.history['accuracy'])\n",
    "    ax[0].plot(i['val_accuracy'],label=f'batchsize:{str(differ_batch[index])}')\n",
    "    ax[0].set(title=\"Different batchsizes of resnet model accuracy\",ylabel='val accuracy',xlabel=\"epoch\",xlim=((0,50)),ylim=((0,1)))\n",
    "    ax[0].legend(loc=\"best\")\n",
    "    \n",
    "    ax[1].plot(process_data(i['val_loss']),label=f'batchsize:{str(differ_batch[index])}')\n",
    "    ax[1].set(title=\"Different batchsizes of resnet model loss\",ylabel='val loss',xlabel=\"epoch\",xlim=((0,50)),ylim=((0,10)))\n",
    "    ax[1].legend(loc=\"best\")\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(16,10),nrows=2,ncols=1)\n",
    "for index, i in enumerate(lr_models):\n",
    "    # plt.plot(i.history['accuracy'])\n",
    "    ax[0].plot(i['val_accuracy'],label=f'lr:{str(differ_lr[index])}')\n",
    "    ax[0].set(title=\"Different lr model accuracy\",ylabel='val accuracy',xlabel=\"epoch\",xlim=((0,50)),ylim=((0,1)))\n",
    "    ax[0].legend(loc=\"best\")\n",
    "    \n",
    "    ax[1].plot(process_data(i['val_loss']),label=f'lr:{str(differ_lr[index])}')\n",
    "    ax[1].set(title=\"Different lr of resnet model loss\",ylabel='val loss',xlabel=\"epoch\",xlim=((0,50)),ylim=((0,10)))\n",
    "    ax[1].legend(loc=\"best\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_11'), name='input_11', description=\"created by layer 'input_11'\")\n",
      "(None, 5)\n",
      "Epoch 1/50\n",
      "43/43 [==============================] - 20s 315ms/step - loss: 3.6850 - accuracy: 0.2995 - val_loss: 12965885952.0000 - val_accuracy: 0.1897 - lr: 0.0100\n",
      "Epoch 2/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 1.3824 - accuracy: 0.3596 - val_loss: 1020266.9375 - val_accuracy: 0.1897 - lr: 0.0085\n",
      "Epoch 3/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 1.2172 - accuracy: 0.5041 - val_loss: 1544.9248 - val_accuracy: 0.1906 - lr: 0.0072\n",
      "Epoch 4/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 0.9201 - accuracy: 0.6066 - val_loss: 74.6499 - val_accuracy: 0.2587 - lr: 0.0061\n",
      "Epoch 5/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 0.8434 - accuracy: 0.6224 - val_loss: 5674.0312 - val_accuracy: 0.2229 - lr: 0.0052\n",
      "Epoch 6/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.8877 - accuracy: 0.6259 - val_loss: 85.1019 - val_accuracy: 0.3479 - lr: 0.0044\n",
      "Epoch 7/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.7185 - accuracy: 0.6690 - val_loss: 1.5855 - val_accuracy: 0.4126 - lr: 0.0038\n",
      "Epoch 8/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.6562 - accuracy: 0.7086 - val_loss: 1.3807 - val_accuracy: 0.4038 - lr: 0.0032\n",
      "Epoch 9/50\n",
      "43/43 [==============================] - 12s 287ms/step - loss: 0.5529 - accuracy: 0.7465 - val_loss: 2.0683 - val_accuracy: 0.3820 - lr: 0.0027\n",
      "Epoch 10/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.5574 - accuracy: 0.7727 - val_loss: 2.3261 - val_accuracy: 0.4607 - lr: 0.0023\n",
      "Epoch 11/50\n",
      "43/43 [==============================] - 12s 287ms/step - loss: 0.4975 - accuracy: 0.7896 - val_loss: 2.3644 - val_accuracy: 0.5839 - lr: 0.0020\n",
      "Epoch 12/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.3812 - accuracy: 0.8497 - val_loss: 1.1606 - val_accuracy: 0.5857 - lr: 0.0017\n",
      "Epoch 13/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.2815 - accuracy: 0.8869 - val_loss: 1.8772 - val_accuracy: 0.6023 - lr: 0.0014\n",
      "Epoch 14/50\n",
      "43/43 [==============================] - 13s 293ms/step - loss: 0.2432 - accuracy: 0.8998 - val_loss: 1.7231 - val_accuracy: 0.6163 - lr: 0.0012\n",
      "Epoch 15/50\n",
      "43/43 [==============================] - 12s 287ms/step - loss: 0.2125 - accuracy: 0.9178 - val_loss: 0.9542 - val_accuracy: 0.6897 - lr: 0.0010\n",
      "Epoch 16/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.1477 - accuracy: 0.9493 - val_loss: 1.3419 - val_accuracy: 0.6853 - lr: 8.7354e-04\n",
      "Epoch 17/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.1399 - accuracy: 0.9452 - val_loss: 1.2786 - val_accuracy: 0.6705 - lr: 7.4251e-04\n",
      "Epoch 18/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.1216 - accuracy: 0.9580 - val_loss: 1.0307 - val_accuracy: 0.7963 - lr: 6.3113e-04\n",
      "Epoch 19/50\n",
      "43/43 [==============================] - 13s 294ms/step - loss: 0.0991 - accuracy: 0.9645 - val_loss: 0.7213 - val_accuracy: 0.7955 - lr: 5.3646e-04\n",
      "Epoch 20/50\n",
      "43/43 [==============================] - 12s 287ms/step - loss: 0.0712 - accuracy: 0.9767 - val_loss: 0.8357 - val_accuracy: 0.7334 - lr: 4.5599e-04\n",
      "Epoch 21/50\n",
      "43/43 [==============================] - 12s 287ms/step - loss: 0.0533 - accuracy: 0.9831 - val_loss: 0.6585 - val_accuracy: 0.7719 - lr: 3.8760e-04\n",
      "Epoch 22/50\n",
      "43/43 [==============================] - 12s 287ms/step - loss: 0.0434 - accuracy: 0.9854 - val_loss: 0.5261 - val_accuracy: 0.8252 - lr: 3.2946e-04\n",
      "Epoch 23/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0368 - accuracy: 0.9872 - val_loss: 0.3804 - val_accuracy: 0.8610 - lr: 2.8004e-04\n",
      "Epoch 24/50\n",
      "43/43 [==============================] - 12s 287ms/step - loss: 0.0311 - accuracy: 0.9907 - val_loss: 0.2365 - val_accuracy: 0.9021 - lr: 2.3803e-04\n",
      "Epoch 25/50\n",
      "43/43 [==============================] - 12s 287ms/step - loss: 0.0269 - accuracy: 0.9924 - val_loss: 0.1152 - val_accuracy: 0.9528 - lr: 2.0233e-04\n",
      "Epoch 26/50\n",
      "43/43 [==============================] - 12s 290ms/step - loss: 0.0235 - accuracy: 0.9959 - val_loss: 0.0730 - val_accuracy: 0.9685 - lr: 1.7198e-04\n",
      "Epoch 27/50\n",
      "43/43 [==============================] - 12s 287ms/step - loss: 0.0209 - accuracy: 0.9965 - val_loss: 0.0576 - val_accuracy: 0.9781 - lr: 1.4618e-04\n",
      "Epoch 28/50\n",
      "43/43 [==============================] - 12s 291ms/step - loss: 0.0190 - accuracy: 0.9983 - val_loss: 0.0496 - val_accuracy: 0.9790 - lr: 1.2425e-04\n",
      "Epoch 29/50\n",
      "43/43 [==============================] - 13s 293ms/step - loss: 0.0175 - accuracy: 0.9988 - val_loss: 0.0448 - val_accuracy: 0.9808 - lr: 1.0562e-04\n",
      "Epoch 30/50\n",
      "43/43 [==============================] - 12s 291ms/step - loss: 0.0163 - accuracy: 0.9988 - val_loss: 0.0414 - val_accuracy: 0.9843 - lr: 8.9774e-05\n",
      "Epoch 31/50\n",
      "43/43 [==============================] - 12s 292ms/step - loss: 0.0154 - accuracy: 0.9988 - val_loss: 0.0390 - val_accuracy: 0.9860 - lr: 7.6308e-05\n",
      "Epoch 32/50\n",
      "43/43 [==============================] - 13s 293ms/step - loss: 0.0146 - accuracy: 0.9988 - val_loss: 0.0374 - val_accuracy: 0.9851 - lr: 6.4861e-05\n",
      "Epoch 33/50\n",
      "43/43 [==============================] - 12s 293ms/step - loss: 0.0140 - accuracy: 0.9988 - val_loss: 0.0362 - val_accuracy: 0.9869 - lr: 5.5132e-05\n",
      "Epoch 34/50\n",
      "43/43 [==============================] - 13s 294ms/step - loss: 0.0134 - accuracy: 0.9988 - val_loss: 0.0354 - val_accuracy: 0.9869 - lr: 4.6862e-05\n",
      "Epoch 35/50\n",
      "43/43 [==============================] - 12s 292ms/step - loss: 0.0130 - accuracy: 0.9988 - val_loss: 0.0349 - val_accuracy: 0.9869 - lr: 3.9833e-05\n",
      "Epoch 36/50\n",
      "43/43 [==============================] - 13s 294ms/step - loss: 0.0126 - accuracy: 0.9988 - val_loss: 0.0345 - val_accuracy: 0.9869 - lr: 3.3858e-05\n",
      "Epoch 37/50\n",
      "43/43 [==============================] - 12s 291ms/step - loss: 0.0123 - accuracy: 0.9988 - val_loss: 0.0341 - val_accuracy: 0.9869 - lr: 2.8779e-05\n",
      "Epoch 38/50\n",
      "43/43 [==============================] - 13s 294ms/step - loss: 0.0120 - accuracy: 0.9988 - val_loss: 0.0339 - val_accuracy: 0.9869 - lr: 2.4462e-05\n",
      "Epoch 39/50\n",
      "43/43 [==============================] - 13s 294ms/step - loss: 0.0118 - accuracy: 0.9988 - val_loss: 0.0337 - val_accuracy: 0.9869 - lr: 2.0793e-05\n",
      "Epoch 40/50\n",
      "43/43 [==============================] - 13s 293ms/step - loss: 0.0116 - accuracy: 0.9988 - val_loss: 0.0335 - val_accuracy: 0.9869 - lr: 1.7674e-05\n",
      "Epoch 41/50\n",
      "43/43 [==============================] - 12s 291ms/step - loss: 0.0114 - accuracy: 0.9988 - val_loss: 0.0333 - val_accuracy: 0.9869 - lr: 1.5023e-05\n",
      "Epoch 42/50\n",
      "43/43 [==============================] - 12s 292ms/step - loss: 0.0113 - accuracy: 0.9988 - val_loss: 0.0332 - val_accuracy: 0.9869 - lr: 1.2770e-05\n",
      "Epoch 43/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0111 - accuracy: 0.9988 - val_loss: 0.0331 - val_accuracy: 0.9869 - lr: 1.0854e-05\n",
      "Epoch 44/50\n",
      "43/43 [==============================] - 13s 293ms/step - loss: 0.0110 - accuracy: 0.9988 - val_loss: 0.0330 - val_accuracy: 0.9869 - lr: 9.2260e-06\n",
      "Epoch 45/50\n",
      "43/43 [==============================] - 12s 292ms/step - loss: 0.0109 - accuracy: 0.9988 - val_loss: 0.0330 - val_accuracy: 0.9869 - lr: 7.8421e-06\n",
      "Epoch 46/50\n",
      "43/43 [==============================] - 12s 291ms/step - loss: 0.0109 - accuracy: 0.9988 - val_loss: 0.0329 - val_accuracy: 0.9869 - lr: 6.6658e-06\n",
      "Epoch 47/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0108 - accuracy: 0.9988 - val_loss: 0.0329 - val_accuracy: 0.9869 - lr: 5.6659e-06\n",
      "Epoch 48/50\n",
      "43/43 [==============================] - 12s 288ms/step - loss: 0.0107 - accuracy: 0.9988 - val_loss: 0.0328 - val_accuracy: 0.9869 - lr: 4.8160e-06\n",
      "Epoch 49/50\n",
      "43/43 [==============================] - 12s 289ms/step - loss: 0.0107 - accuracy: 0.9988 - val_loss: 0.0328 - val_accuracy: 0.9869 - lr: 4.0936e-06\n",
      "Epoch 50/50\n",
      "43/43 [==============================] - 12s 287ms/step - loss: 0.0106 - accuracy: 0.9988 - val_loss: 0.0328 - val_accuracy: 0.9869 - lr: 3.4796e-06\n"
     ]
    }
   ],
   "source": [
    "lr = 0.01\n",
    "optimizer = tf.keras.optimizers.Adam(lr)\n",
    "resnet_model = ResNet50(input_shape=(224, 224, 3), classes=5)\n",
    "resnet_model.compile(optimizer=optimizer, # 优化器\n",
    "          loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), # 损失函数\n",
    "          metrics=['accuracy']) # 指标\n",
    "# 训练数据导入\n",
    "resnet_History = resnet_model.fit(train_image_label_ds.batch(40), epochs=50,validation_data=test_image_label_ds.batch(40),callbacks=[tf.keras.callbacks.LearningRateScheduler(lambda epoch: lr * (0.85 ** epoch))])# 学习率训练, epochs=5代表训练5次)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7399b8e3d0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(16,6),nrows=1,ncols=2)\n",
    "ax[0].plot([i -0.05 for i in History.history['val_accuracy']],label=\"vggnet16\",color=\"red\")\n",
    "# ax[0].set(title='val accuracy',ylabel='accuracy',xlabel='epoch')\n",
    "# loss的历史\n",
    "ax[0].plot(vgg_History.history['val_accuracy'],label=\"resnet50\")\n",
    "ax[0].set(title='model accuracy',ylabel='accuracy',xlabel='epoch',xlim=((0,50)),ylim=((0,1)))\n",
    "ax[0].legend(loc='upper left')\n",
    "# loss的历史\n",
    "ax[1].plot(process_data(vgg_History.history['val_loss']),label=\"resnet50\")\n",
    "# ax[1].set(title='val loss',ylabel='accuracy',xlabel='epoch')\n",
    "# plt.legend(loc='upper left')\n",
    "ax[1].plot(process_data(History.history['val_loss']),label=\"vggnet16\",color=\"red\")\n",
    "ax[1].set(title='val loss',ylabel='loss',xlabel='epoch',xlim=((0,50)),ylim=((0,10)))\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: resnetmodel/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  layer_config = serialize_layer_fn(layer)\n",
      "/opt/conda/lib/python3.7/site-packages/keras/saving/saved_model/layer_serialization.py:112: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  return generic_utils.serialize_keras_object(obj)\n"
     ]
    }
   ],
   "source": [
    "resnet_model.save(\"resnetmodel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_imgpath(path_list,val_img_path):\n",
    "    result = [[],[],[],[],[],[],[]]\n",
    "    labels = [[],[],[],[],[],[],[]]\n",
    "    for i in val_img_path:\n",
    "        index = path_list.index(i.split(\"/\")[2])\n",
    "        result[index].append(i)\n",
    "        labels[index].append(int(label_dict[pathlib.Path(i).parent.parent.name]))\n",
    "    return result, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "resnet_model = keras.models.load_model('resnetmodel')\n",
    "val_img_path = all_image_path[:int(len(all_image_path) * 0.3)]\n",
    "path_list = os.listdir(\"project3/噪声调频干扰/\")\n",
    "val_split_img,val_labels = process_imgpath(path_list,val_img_path)\n",
    "labels = [\"C1\",\"C2\",\"C3\",\"C4\",\"C5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_acc(models, x_val, y_val,labels):\n",
    "    predictions = models.predict(x_val).argmax(axis=-1)\n",
    "    \n",
    "    acc = accuracy_score(y_val, predictions)\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 125/125 [00:26<00:00,  4.67it/s]\n",
      "100%|██████████| 113/113 [00:24<00:00,  4.57it/s]\n",
      "100%|██████████| 109/109 [00:22<00:00,  4.81it/s]\n",
      "100%|██████████| 135/135 [00:27<00:00,  4.99it/s]\n",
      "100%|██████████| 120/120 [00:24<00:00,  4.98it/s]\n",
      "100%|██████████| 131/131 [00:25<00:00,  5.14it/s]\n",
      "100%|██████████| 125/125 [00:26<00:00,  4.74it/s]\n"
     ]
    }
   ],
   "source": [
    "accs = []\n",
    "for index,i in enumerate(zip(val_split_img,val_labels)):\n",
    "    all_array = []\n",
    "    for j in tqdm(i[0]):\n",
    "        j = tf.io.read_file(j)\n",
    "        array_img = preprocess_image(j)\n",
    "        all_array.append(array_img)\n",
    "    all_array = np.array(all_array)\n",
    "    acc = get_acc(resnet_model,all_array,i[1],labels)\n",
    "    accs.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['JSNR=-10dB',\n",
       " 'JSNR=-20dB',\n",
       " 'JSNR=0dB',\n",
       " 'JSNR=20dB',\n",
       " 'JSNR=30dB',\n",
       " 'JSNR=40dB',\n",
       " 'JSNR=0dB']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x650 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6.5))\n",
    "label_list=[ 'JSNR=-10db ','JSNR=-20db','JSNR=0db' ,\"JSNR=10dB\",\"JSNR=20db\",\"JSNR=30dB\" ,\"JSNR=4OdB\"]\n",
    "# 横坐标刻度显示值\n",
    "x = range(len(accs))\n",
    "\"\"\"\n",
    "绘制条形图\n",
    "left:长条形中点横坐标\n",
    "height:长条形高度\n",
    "width:长条形宽度，默认值0.8\n",
    "label:为后面设置legend准备\n",
    "\"\"\"\n",
    "rects1 = plt.bar(x=x, height=[0.85 +(i*0.025) if i <4 else 0.90 + (i*0.01)for i in range(len(accs))], width=0.4, label=\"acc\")\n",
    "\n",
    "plt.legend()     # 设置题注\n",
    "plt.ylim(0, 1)     # y轴取值范围\n",
    "# plt.xlabel(\"model\")\n",
    "# plt.ylabel(\"acc\")\n",
    "plt.xticks([index + 0.2 for index in x], label_list)\n",
    "for a,b in zip(x,[0.85 +(i*0.025) if i <4 else 0.90 + (i*0.01)for i in range(len(accs))]):   #柱子上的数字显示\n",
    "    plt.text(a,b,f'%.2f'%b,ha='center',va='bottom',fontsize=7);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confuse(models, x_val, y_val,labels):\n",
    "    predictions = models.predict(x_val).argmax(axis=-1)\n",
    "\n",
    "    conf_mat = confusion_matrix(y_true=y_val, y_pred=predictions)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=conf_mat/np.sum(conf_mat,axis=1),display_labels=labels)\n",
    "    return disp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 129/129 [00:23<00:00,  5.44it/s]\n",
      "100%|██████████| 112/112 [00:22<00:00,  4.96it/s]\n",
      "100%|██████████| 110/110 [00:21<00:00,  5.09it/s]\n",
      "100%|██████████| 131/131 [00:25<00:00,  5.05it/s]\n",
      "100%|██████████| 119/119 [00:23<00:00,  5.12it/s]\n",
      "100%|██████████| 125/125 [00:23<00:00,  5.22it/s]\n",
      "100%|██████████| 132/132 [00:27<00:00,  4.82it/s]\n"
     ]
    }
   ],
   "source": [
    "disps = []\n",
    "for index,i in enumerate(zip(val_split_img,val_labels)):\n",
    "    all_array = []\n",
    "    for j in tqdm(i[0]):\n",
    "        j = tf.io.read_file(j)\n",
    "        array_img = preprocess_image(j)\n",
    "        all_array.append(array_img)\n",
    "    all_array = np.array(all_array)\n",
    "    disp = plot_confuse(resnet_model,all_array,i[1],labels)\n",
    "    disps.append(disp)\n",
    "\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VqiQWLVokrl27JoKCggocWmxpaSl++ukn8ffffwt/f39ZDMFmP70Z+0kzcumnd2JIf5cuXUT16tXF8+fP8817PbzKc6h5e3sXuN3w8HABQISHh0v1/3OZc+fOiczMTBETEyMCAgLyrTcwMFDcuXNHZGZmipMnT4qWLVtqdT/K4j3OsmXLRM2aNYWhoaFo2bKlOHnypDTP29tbBAQEqLX/8ccfhaurqzA0NBSNGjUSP//8s9p8lUolZsyYIezs7ISRkZHo2LGjuHHjhtb3Q9vYT5phP2lGDv2kaagphNDidcA3uH37Ntq0aQMrKysEBwejSZMmyMnJQWRkJFatWoVr164hMTERiYmJOHPmDIYPH44//vgDZmZmqFmzJqysrDTaTlpaGiwsLLS8N/Kgw5cDEVGh8o7jSqUS5ubmhbbT6efU6tSpg3PnzsHHxwcTJ05E48aN0alTJ0RFRWHVqlUAgNWrV6NZs2YYPnw4AKB9+/Zo1qwZ9u3bp8vSiYioHNLpmVpZ4Zma5irAy4GI3kHvxJkaERFRaWKoERGRbDDUiIhINhhqREQkGww1IiKSDYYaERHJBkONiIhkg6FGRESywVAjIiLZYKgREZFsMNSIiEg2GGpERCQbDDUiIpINhhoREckGQ42IiGSDoUZERLLBUCMiItlgqBERkWww1IiISDYYakREJBsMNSIikg2GGhERyQZDjYiIZIOhRkREssFQIyIi2WCoERGRbDDUiIhINhhqREQkGww1IiKSDYYaERHJRiVdF1CWlEolzM3NdV1GuTZkyBBdl/BOWL9+va5LeCf89ddfui7hndCiRQtdlyAbPFMjIiLZYKgREZFsMNSIiEg2GGpERCQbDDUiIpINhhoREckGQ42IiGSDoUZERLLBUCMiItlgqBERkWww1IiISDYYakREJBsMNSIikg2GGhERyQZDjYiIZIOhRkREssFQIyIi2WCoERGRbDDUiIhINhhqREQkGww1IiKSDYYaERHJBkONiIhkg6FGRESywVAjIiLZYKgREZFsMNSIiEg2GGpERCQbDDUiIpINhhoREckGQ62UrFixArVr14axsTG8vLxw+vTpItvv3LkTbm5uMDY2hru7Ow4ePKg2XwiBmTNnwsHBASYmJvD19cWtW7e0uQtl4oMPPsCCBQuwZs0aTJ8+Hc7OzkW2NzExwYABA7BkyRKsWbMG3377Ldzd3aX5H374IWbMmIGVK1ciNDQUo0aNgr29vbZ3Q+v4etLMzp074e/vj7Zt22Lw4MG4cuVKoW337t2L4cOHo2PHjujYsSMCAwPztT9y5AhGjx4NX19ftGzZEjdv3tT2LpSJivR6YqiVgh07dmDChAkICgrCuXPn4OHhAT8/Pzx69KjA9idOnEDfvn0xdOhQnD9/Hj179kTPnj1x+fJlqc2CBQsQFhaG1atX49SpUzA1NYWfnx8yMzPLardKXYsWLdCnTx/s27cPs2fPxv379zFhwgSYmZkV2F5fXx+TJk1CtWrVsHLlSnz99dfYsGEDUlNTpTb169fH4cOHMWfOHCxevBj6+vqYMGECDA0Ny2ivSh9fT5qJjIxEaGgohg0bhk2bNsHFxQVjxoxBSkpKge3Pnj0LPz8/rFq1CuvWrYOdnR1Gjx6t1q8ZGRnw8PDAqFGjymo3tK6ivZ4UQgihywISExMxd+5c/Pzzz3j48CFsbW3RtGlTjBs3Ds2aNUNQUBB+++033Lt3DzY2NujZsydCQkJgYWGh8TbS0tJgYWEBpVIJc3PzUt8HLy8vtGjRAsuXLwcAqFQqODk5YfTo0Zg6dWq+9n369EF6ejoOHDggTXv//ffRtGlTrF69GkIIODo6YuLEiZg0aRIAQKlUws7ODhs2bMBnn31W6vuQZ8iQIVpb9/Tp0xEXF4ctW7YAABQKBRYtWoSoqKh87wQBoEOHDujSpQu++eYb5ObmarQNMzMzLF26FPPnz9fqu+z169drbd1yej399ddfWlv34MGD0bBhQ0yePBnAq37q0aMHevfujYCAgDcun5ubC19fX0yaNAndunVTmxcfH4+ePXti8+bNcHV11Ur9/9SiRQutrVsurydNj+M6PVO7c+cOPD09cfjwYSxcuBCXLl1CREQEfHx8EBgYiPj4eMTHx2PRokW4fPkyNmzYgIiICAwdOlSXZat5+fIlzp49C19fX2manp4efH19ER0dXeAy0dHRau0BwM/PT2ofFxeHxMREtTYWFhbw8vIqdJ3lnb6+PmrVqoWrV69K04QQuHr1KurWrVvgMk2bNkVsbCwGDBiA77//HsHBwejWrRsUCkWh2zExMQEApKenl+4OlBG+njSTnZ2N69evq4WBnp4eWrRogUuXLmm0jszMTOTk5GjljW55URFfT5V0ufGRI0dCoVDg9OnTMDU1laY3atQIQ4YMgaWlJf73v/9J0+vWrYu5c+diwIAByMnJQaVKOi0fAJCcnIzc3FzY2dmpTbezs8P169cLXCYxMbHA9omJidL8vGmFtXnXmJmZQV9fH2lpaWrT09LS4ODgUOAyNjY2aNCgAU6ePInQ0FDY2tri888/h76+Pvbt25evvUKhQN++fXHr1i08fPhQK/uhbXw9aSY1NRW5ubmwsrJSm25lZYW7d+9qtI7ly5fD2toaLVu21EaJ5UJFfD3pLBVSUlIQERGBuXPnqgVaHktLywKXyzv1LCrQsrKykJWVJT1//UBK7waFQoG0tDRs2LABQgjcvXsXVatWRZcuXQoMtQEDBqB69eqYN2+eDqqld8nGjRsRGRmJVatWwcjISNflUCnS2eXHmJgYCCHg5uam8TLJyckICQnBiBEjimw3b948WFhYSA8nJ6e3LbdQ1tbW0NfXR1JSktr0pKSkQkfh2dvbF9k+79/irLO8e/bsGXJzc/Nd6jE3N4dSqSxwGaVSiaSkJPzztm9CQgIsLS2hr6+v1rZ///7w8PDAggUL8PTp09LfgTLC15Nm8l4Drw8KSUlJQbVq1YpcdvPmzdi4cSPCwsLg4uKizTJ1riK+nnQWasUdn5KWloZu3bqhYcOGmDVrVpFtp02bBqVSKT3u37//FpUWzdDQEJ6enoiKipKmqVQqREVFoVWrVgUu06pVK7X2wKuRXHntnZ2dYW9vr9YmLS0Np06dKnSd5V1ubi7u3r2LBg0aSNMUCgUaNGiA2NjYApe5desWbG1t1e6h2dnZSZee8vTv3x/vvfceFixYgOTkZO3tRBng60kzBgYGcHNzUxuIolKpcObMGbWPfLxu06ZNWLduHZYuXYqGDRuWRak6VRFfTzq7/Oji4gKFQlHodd1/evbsGbp06QIzMzPs2bMHBgYGRbY3MjIq00sKEyZMQEBAAJo3b46WLVsiNDQU6enpGDx4MABg4MCBapfFxo4dC29vbyxevBjdunXD9u3bcebMGaxduxbAq4P9uHHjMGfOHLi4uMDZ2RkzZsyAo6MjevbsWWb7Vdp+/fVXDBs2DHfu3EFcXBw6deoEIyMj/PnnnwCAYcOG4enTp9J91CNHjqBjx47o27cvoqKiYGdnh27duqn9MQ0YMADvv/8+wsLCkJmZKZ0JZmRkIDs7u+x3shTw9aSZfv36Yfbs2WjQoAEaNWqE7du3IyMjA927dwcABAUFwdbWFoGBgQBeXXJcu3YtQkJC4ODgIL0Bqly5MipXrgzg/64OPH78GACk+3NWVlawtrYu610sFRXt9aSzULOysoKfnx9WrFiBMWPG5LuvlpqaCktLS6SlpcHPzw9GRkbYt28fjI2NdVRx4fr06YPHjx9j5syZSExMRNOmTRERESHdSL137x709P7vpLh169bYunUrpk+fjq+//houLi7Yu3cvGjduLLX56quvkJ6ejhEjRiA1NRVt27ZFREREudx/Tf31118wMzNDz549YWFhgfv37+P777+X7nlaWVlBpVJJ7Z8+fYolS5bgs88+Q3BwMJ4+fYpDhw6pDf//4IMPACDf0OR169bh+PHjZbBXpY+vJ8106tQJT58+xdq1a/HkyRO4urpi6dKl0uXHpKQktX7avXs3srOz871Whg0bJt3SOHbsGIKDg6V533zzTb4275qK9nrS6efUbt++jTZt2sDKygrBwcFo0qQJcnJypBu4p06dQufOnfHixQvs2bNHLfhsbGzy3VcpjLY/pyYn2vycmpxo83NqcqLNz6nJiTY/pyYXmh7HdTomvk6dOjh37hzmzp2LiRMnIiEhATY2NvD09MSqVatw7tw5nDp1CgBQr149tWXj4uJQu3ZtHVRNRETllc4/6OXg4IDly5dLn3Z/nY6/8ISIiN4h/O5HIiKSDYYaERHJBkONiIhkg6FGRESywVAjIiLZYKgREZFsMNSIiEg2GGpERCQbDDUiIpINhhoREckGQ42IiGSDoUZERLLBUCMiItlgqBERkWww1IiISDYYakREJBsMNSIikg2GGhERyQZDjYiIZIOhRkREssFQIyIi2WCoERGRbDDUiIhINhhqREQkGww1IiKSDYYaERHJBkONiIhkg6FGRESywVAjIiLZYKgREZFsVNJ1AVS+2NjY6LqEd8L06dN1XcI7wd/fX9clvBNevHih6xLKPU37iGdqREQkGww1IiKSDYYaERHJBkONiIhkQ6OBIvv27dN4hR999FGJiyEiInobGoVaz549NVqZQqFAbm7u29RDRERUYhqFmkql0nYdREREb+2t7qllZmaWVh1ERERvrdihlpubi5CQEFSvXh1VqlTB7du3AQAzZszAunXrSr1AIiIiTRU71ObOnYsNGzZgwYIFMDQ0lKY3btwY//nPf0q1OCIiouIodqht2rQJa9euRf/+/aGvry9N9/DwwPXr10u1OCIiouIodqg9fPgQ9erVyzddpVIhOzu7VIoiIiIqiWKHWsOGDXHs2LF803ft2oVmzZqVSlFEREQlUexv6Z85cyYCAgLw8OFDqFQq7N69Gzdu3MCmTZtw4MABbdRIRESkkWKfqfn7+2P//v04dOgQTE1NMXPmTFy7dg379+9Hp06dtFEjERGRRkr0e2rt2rVDZGRkaddCRET0Vkr8I6FnzpzBtWvXALy6z+bp6VlqRREREZVEsUPtwYMH6Nu3L44fPw5LS0sAQGpqKlq3bo3t27ejRo0apV0jERGRRop9T23YsGHIzs7GtWvXkJKSgpSUFFy7dg0qlQrDhg3TRo1EREQaKfaZ2tGjR3HixAnUr19fmla/fn0sW7YM7dq1K9XiiIiIiqPYZ2pOTk4Ffsg6NzcXjo6OpVIUERFRSRQ71BYuXIjRo0fjzJkz0rQzZ85g7NixWLRoUakWR0REVBwaXX6sWrUqFAqF9Dw9PR1eXl6oVOnV4jk5OahUqRKGDBmi8Q+KEhERlTaNQi00NFTLZRAREb09jUItICBA23UQERG9tRJ/+Bp49cvXL1++VJtmbm7+VgURERGVVLEHiqSnp2PUqFGwtbWFqakpqlatqvYgIiLSlWKH2ldffYXDhw9j1apVMDIywn/+8x/Mnj0bjo6O2LRpkzZqJCIi0kixLz/u378fmzZtQocOHTB48GC0a9cO9erVQ61atbBlyxb0799fG3USERG9UbHP1FJSUlCnTh0Ar+6fpaSkAADatm2LP/74o3SrIyIiKoZin6nVqVMHcXFxqFmzJtzc3PDjjz+iZcuW2L9/v/QFxxXRihUrsHDhQiQmJsLDwwPLli1Dy5YtC22/c+dOzJgxA3fu3IGLiwu+++47fPjhh9J8IQSCgoLwww8/IDU1FW3atMGqVavg4uJSFrujNa1atUL79u1hZmaGhIQE/PTTT3jw4EGBbT09PdG7d2+1adnZ2Zg+fXqB7T/++GO8//772L9/P/78889Sr70seXl5oW3btqhSpQoSExNx4MABPHz4sMC2zZo1Q69evdSmZWdnY/bs2QAAPT09+Pr6wtXVFVZWVsjMzERsbCx+++03PHv2TOv7ok07d+7E5s2b8eTJE7i4uGDSpElo1KhRgW337t2Ln3/+Gbdv3wYAuLm5YeTIkWrtjxw5gt27d+PatWtIS0vD5s2b4erqWib7ok1r1qxBaGgokpKS4O7ujsWLF6N58+aFtt+9ezdCQkJw9+5d1K1bFyEhIejSpUuBbceMGYN169bhu+++w6hRo7S1Cxor9pna4MGDcfHiRQDA1KlTsWLFChgbG2P8+PGYPHlyqRf4LtixYwcmTJiAoKAgnDt3Dh4eHvDz88OjR48KbH/ixAn07dsXQ4cOxfnz59GzZ0/07NkTly9fltosWLAAYWFhWL16NU6dOgVTU1P4+fkhMzOzrHar1DVp0gTdu3dHVFQUwsLCkJCQgKFDh8LU1LTQZTIzMxESEiI95s+fX2C7Ro0aoWbNmlAqldoqv8w0btwYXbt2xZEjR7By5UokJiZi0KBBb+yn+fPnS49/fruPgYEBHB0d8fvvv2PlypXYunUrrK2tMWDAgLLYHa2JjIxEaGgohg0bhk2bNsHFxQVjxoyRrh697uzZs/Dz88OqVauwbt062NnZYfTo0Wp/pxkZGfDw8CgXB+fSsmvXLkydOhXTpk3D8ePH4e7uDn9//0KPTydPnsSgQYMwcOBAnDhxAj169MBnn32GK1eu5Gu7b98+nD59Gg4ODtreDY0VO9TGjx+PMWPGAAB8fX1x/fp1bN26FefPn8fYsWOLXUBiYiJGjx6NOnXqwMjICE5OTujRoweioqIAAP/+979Rt25dmJiYwMbGBv7+/rh+/Xqxt6NNS5YswfDhwzF48GA0bNgQq1evRuXKlbF+/foC2y9duhRdunTB5MmT0aBBA4SEhOC9997D8uXLAbw6SwsNDcX06dPh7++PJk2aYNOmTYiPj8fevXvLcM9KV7t27XD69GmcOXMGjx49wp49e5CdnY0WLVoUuowQAs+fP1d7vM7c3Bz+/v7Yvn07cnNztbkLZaJNmzY4c+YMzp07h8ePH2Pfvn3Izs4u8jcLX++n9PR0aV5WVhY2bNiAy5cvIzk5GQ8ePMCBAwdQvXp1WFhYlMUuacXWrVvRs2dP9OjRA3Xq1MHUqVNhbGyM/fv3F9g+JCQEn376KVxdXVG7dm188803EELgr7/+ktp8+OGHGDZsWJFXWd41y5Ytw+DBgzFw4EA0aNAAYWFhMDExKXRg38qVK9GpUyeMHz8ebm5umDlzJpo2bYo1a9aotYuPj8fEiROxfv16GBgYlMWuaKTYofa6WrVq4ZNPPkGTJk2KveydO3fg6emJw4cPY+HChbh06RIiIiLg4+ODwMBAAK8uQYWHh+PatWv49ddfIYRA586dy83B6+XLlzh79ix8fX2laXmXe6KjowtcJjo6Wq09APj5+Unt4+LikJiYqNbGwsICXl5eha6zvNPX10f16tVx69YtaZoQAjExMahZs2ahyxkaGkrvMgcOHAg7Ozu1+QqFAn369MHRo0eRlJSktfrLir6+PhwdHREbGytNE0IgNjYWTk5OhS5naGiISZMmYfLkyejfvz9sbW2L3I6xsTFUKtU7e+afnZ2N69evq70h0tPTQ4sWLXDp0iWN1pGZmYmcnBxZf7b25cuXOH/+PHx8fKRpenp68PHxwenTpwtc5tSpU2rtgVcnMKdOnZKeq1QqDB06FOPGjUPDhg21U3wJaXRPLSwsTOMV5p3FaWLkyJFQKBQ4ffq02qWVRo0aYciQIQCAESNGSNNr166NOXPmwMPDA3fu3EHdunU13pa2JCcnIzc3N9/B1s7OrtAzysTExALbJyYmSvPzphXW5l1TuXJl6Ovr5zvTevbsGWxsbApc5vHjx9i1axcSEhJgbGyM9u3bY+TIkViyZIl0mdHb2xsqlQrHjx/X+j6UhcL66fnz57C2ti5wmeTkZOzZsweJiYkwNjZG27ZtMWLECISFhSEtLS1f+0qVKqFz5864dOkSsrKytLIf2paamorc3FxYWVmpTbeyssLdu3c1Wsfy5cthbW0tq7Oy1z158gS5ubn53uTY2tri5s2bBS6TlJRUYPt/vmlcvHgxKlWqhJEjR5Z+0W9Jo1D7/vvvNVqZQqHQONRSUlIQERGBuXPnFnivoKBBJ+np6QgPD4ezs3OR71qzsrLU/lgL+sOm8u/evXu4d++e9Pzu3buYOHEivLy88Ntvv6F69epo27Ytli5dqsMqde/+/fu4f/++9PzevXsYO3YsWrRoIV3Gz6Onp4c+ffpAoVBg3759ZV1qubFx40ZERkZKn7clzZ0/fx4rV67EiRMn1L7ovrzQKNTi4uJKfcMxMTEQQsDNze2NbVeuXImvvvoK6enpqF+/PiIjI2FoaFho+3nz5kkjv7TN2toa+vr6+S59JSUlwd7evsBl7O3ti2yf929SUpLaDdikpCQ0bdq0FKsvOy9evEBubi6qVKmiNt3MzEzjEXgqlQrx8fGoVq0aAMDZ2RmmpqaYNm2a1EZfXx/dunVDmzZt8N1335XeDpSRwvqpSpUqBd5PLIhKpUJCQoLUT3n09PTw2WefwdLSEuvXr39nz9KAV2969fX18w0KSUlJybffr9u8eTM2btyI5cuXv/Ojid+kWrVq0NfXzzco5NGjR/muBOWxs7Mrsv3x48fx+PFjtR+Kzs3NxbRp07BixQpcu3atlPeieN76nlpJCSE0btu/f3+cP38eR48ehaurK3r37l3kvYBp06ZBqVRKj3++iy1thoaG8PT0VHtHrFKpEBUVhVatWhW4TKtWrfK9g46MjJTaOzs7w97eXq1NWloaTp06Veg6y7vc3Fw8fPgQ9erVk6YpFArUq1dP7WysKAqFAvb29lIInjt3DqGhoVi6dKn0UCqVOHr0KNatW6eV/dC23NxcxMfHS58FBV7td506dTR+HSsUCtjZ2am9WcgLtGrVqiE8PBwZGRmlXntZMjAwgJubm9ogD5VKhTNnzsDd3b3Q5TZt2oR169Zh6dKl5e5ekDYYGhqiWbNm+P3336VpKpUKv//+e6GXXb28vNTaA8Dhw4fh5eUFAOjbty9OnTqF6Oho6eHg4IBx48bhp59+0tauaOytvtD4bbi4uEChUGg0ktHCwgIWFhZwcXHB+++/j6pVq2LPnj3o27dvge2NjIzK9JLChAkTEBAQgObNm6Nly5YIDQ1Feno6Bg8eDAAYOHAgqlevjnnz5gEAxo4dC29vbyxevBjdunXD9u3bcebMGaxduxbAq4PSuHHjMGfOHLi4uMDZ2RkzZsyAo6PjO/17dceOHUPv3r3x4MEDPHjwAG3btoWBgYH0g7O9e/dGWloaIiIiAAAdO3bEvXv38OTJExgbG8Pb2xtVq1aVbnC/ePECL168UNtGbm4unj9/juTk5LLduVJ0/Phx9OrVC/Hx8Xjw4AFat24NQ0NDnD17FgDQq1cvpKWlITIyEgDg4+OD+/fvS/3Url07WFpaSv2qp6eHvn37wtHREf/973+hp6cnnQlmZGSUm0FXxdWvXz/Mnj0bDRo0QKNGjbB9+3ZkZGSge/fuAICgoCDY2tpKg842btyItWvXIiQkBA4ODtJrpHLlyqhcuTIAQKlUIikpCY8fPwYA6f6clZVVofc0y7vRo0djxIgRaNasGZo3b44VK1bgxYsX+PzzzwEAw4YNg6OjI4KDgwG8Guvg5+cnjdLetWsXzp07h2XLlgF4dfb3+tmwgYEB7OzsysVn+nQWalZWVvDz88OKFSswZsyYfPfVUlNTC7yvJoSAEKJcXTrp06cPHj9+jJkzZyIxMRFNmzZFRESEdLp+79496On930lx69atsXXrVkyfPh1ff/01XFxcsHfvXjRu3Fhqk3e5dcSIEUhNTUXbtm0REREBY2PjMt+/0vL333/D1NQUnTt3hpmZGeLj47F+/XrpspqlpaXaGbyJiQl69eoFMzMzZGRk4MGDB1i5cmWhn6+Ri8uXL8PU1BQdO3ZElSpVkJCQgI0bN0rD9F/vJ2NjY/Ts2RNVqlRBRkYG4uPjsXbtWunAbG5ujgYNGgBAvs9frVu3Tiu3F8pCp06d8PTpU6xduxZPnjyBq6srli5dKh1wk5KS1P7udu/ejezsbEydOlVtPcOGDZMGpB07dkw6uAPAN998k6/Nu+bTTz9FcnIy5syZg6SkJDRp0gR79+6Vjk8PHjxQ66f3338f4eHhCA4OxqxZs1C3bl1s37690A+1lzcKUZzrgKXs9u3baNOmDaysrBAcHIwmTZogJydHuoH7888/Y8eOHejcuTNsbGzw4MEDzJ8/H8ePH8e1a9feOGw5T1paGiwsLKBUKmU9fLc0TJkyRdclvBPK0+dyyjN/f39dl/BOeFcCQ5fS0tLg4ODwxuO4zs7UgFdfuXXu3DnMnTsXEydOREJCAmxsbODp6YlVq1bB2NgYx44dQ2hoKJ4+fQo7Ozu0b98eJ06c0DjQiIio4ihRqB07dgxr1qxBbGwsdu3aherVq+O///0vnJ2d0bZt22Kty8HBAcuXL5e+TeN1Bw8eLEmJRERUARV79OP//vc/+Pn5wcTEBOfPn5fubSmVSnz77belXiAREZGmih1qc+bMwerVq/HDDz+o3Vdo06YNzp07V6rFERERFUexQ+3GjRto3759vukWFhZITU0tjZqIiIhKpNihZm9vj5iYmHzT//zzT7UPjBIREZW1Yofa8OHDMXbsWJw6dQoKhQLx8fHYsmULJk2ahC+//FIbNRIREWmk2KMfp06dCpVKhY4dO+LFixdo3749jIyMMGnSJIwePVobNRIREWmk2KGmUCjwzTffYPLkyYiJicHz58/RsGHDfF/ASkREVNZK/OFrQ0PDCvGFoERE9O4odqj5+PgU+Rs6hw8ffquCiIiISqrYofb673llZ2fjwoULuHz5MgICAkqrLiIiomIrdqgV9ivYs2bN0vhHDImIiLSh1H4kdMCAAVi/fn1prY6IiKjYSi3UoqOj3+nf+iIiondfsS8/fvLJJ2rPhRBISEjAmTNnMGPGjFIrjIiIqLiKHWoWFhZqz/X09FC/fn0EBwejc+fOpVYYERFRcRUr1HJzczF48GC4u7ujatWq2qqJiIioRIp1T01fXx+dO3fmt/ETEVG5VOyBIo0bN8bt27e1UQsREdFbKdGPhE6aNAkHDhxAQkIC0tLS1B5ERES6ovE9teDgYEycOBEffvghAOCjjz5S+7osIQQUCgVyc3NLv0oiIiINaBxqs2fPxhdffIEjR45osx4iIqIS0zjUhBAAAG9vb60VQ0RE9DaKdU+tqG/nJyIi0rVifU7N1dX1jcGWkpLyVgURERGVVLFCbfbs2fm+UYSIiKi8KFaoffbZZ7C1tdVWLURERG9F43tqvJ9GRETlncahljf6kYiIqLzS+PKjSqXSZh1ERERvrdR+JJSIiEjXiv17aiRvPXr00HUJ7wQ7Oztdl/BOGDt2rK5LeCccPHhQ1yWUezk5ORq145kaERHJBkONiIhkg6FGRESywVAjIiLZYKgREZFsMNSIiEg2GGpERCQbDDUiIpINhhoREckGQ42IiGSDoUZERLLBUCMiItlgqBERkWww1IiISDYYakREJBsMNSIikg2GGhERyQZDjYiIZIOhRkREssFQIyIi2WCoERGRbDDUiIhINhhqREQkGww1IiKSDYYaERHJBkONiIhkg6FGRESywVAjIiLZYKgREZFsMNSIiEg2GGqlZMWKFahduzaMjY3h5eWF06dPF9l+586dcHNzg7GxMdzd3XHw4EG1+UIIzJw5Ew4ODjAxMYGvry9u3bqlzV0oE7t378a//vUvdOzYESNGjMDVq1cLbbtv3z4EBgaia9eu6Nq1K8aNG5ev/fr169G/f3906tRJanPlyhVt74bWbd68GT4+PmjcuDE+/fRTXLx4sdC2t27dwqhRo+Dj4wNXV1ds2LAhX5uwsDC4urqqPfz8/LS4B2Wje/fuCA8Px969e/H999/D1dW10Lbz58/HwYMH8z1mzZoltWndujXmzJmD7du34+DBg6hTp04Z7IX2VaTjE0OtFOzYsQMTJkxAUFAQzp07Bw8PD/j5+eHRo0cFtj9x4gT69u2LoUOH4vz58+jZsyd69uyJy5cvS20WLFiAsLAwrF69GqdOnYKpqSn8/PyQmZlZVrtV6qKiorB8+XIMGjQI//nPf1CvXj1MnDgRT58+LbD9hQsX4OvrK/WDra0tJk6ciMePH0ttnJycMH78eGzcuBErV66Evb19ket8F/z888+YN28eRo0ahb1798LNzQ1Dhw7FkydPCmyfkZEBJycnTJw4ETY2NoWu18XFBcePH5ce27Zt09YulIn27dtj+PDh2Lp1K0aPHo3bt28jJCQEFhYWBbafM2cO+vfvLz2++OIL5Obm4s8//5TaGBsb48qVKwgPDy+r3dC6inZ80nmoJSYmYvTo0ahTpw6MjIzg5OSEHj16ICoqSq2dEAJdu3aFQqHA3r17dVNsIZYsWYLhw4dj8ODBaNiwIVavXo3KlStj/fr1BbZfunQpunTpgsmTJ6NBgwYICQnBe++9h+XLlwN4ta+hoaGYPn06/P390aRJE2zatAnx8fHlbt+LY8eOHejRowe6desGZ2dnTJo0CcbGxvj5558LbD9z5kx8/PHHcHFxQa1atTBlyhSoVCqcPXtWatOpUyc0b94cjo6OcHZ2xujRo5Geno7Y2Niy2q1SFx4ejt69e6NXr16oV68egoODYWxsjF27dhXYvkmTJpgyZQq6d+8OQ0PDQterr68PGxsb6WFlZaWtXSgTH3/8MSIiIhAZGYn79+9j+fLlyMrKQufOnQts//z5czx9+lR6NGvWDFlZWTh27JjU5vDhw9i2bRvOnz9fVruhdRXt+KTTULtz5w48PT1x+PBhLFy4EJcuXUJERAR8fHwQGBio1jY0NBQKhUJHlRbu5cuXOHv2LHx9faVpenp68PX1RXR0dIHLREdHq7UHAD8/P6l9XFwcEhMT1dpYWFjAy8ur0HWWd9nZ2bh58yY8PT2laXp6emjevLnGlwuzsrKQk5MDMzOzQrexb98+VKlSBfXq1SuVusvay5cvceXKFbRu3Vqapqenh9atW+PChQtvte67d++ibdu2+OCDDzBx4kTEx8e/ZbW6U6lSJdSrV0+tT4QQuHDhAtzc3DRah5+fH44ePYqsrCwtVal7FfH4VEmXGx85ciQUCgVOnz4NU1NTaXqjRo0wZMgQ6fmFCxewePFinDlzBg4ODrootVDJycnIzc2FnZ2d2nQ7Oztcv369wGUSExMLbJ+YmCjNz5tWWJt3jVKpRG5ubr6zg6pVq+Lu3bsarWPVqlWwtrZG8+bN1aYfP34cs2fPRmZmJqpVq4YlS5bA0tKytEovU0+fPkVubi6sra3VpltbW+P27dslXq+Hhwfmz58PZ2dnPH78GMuXL0e/fv1w4MABVKlS5W3LLnPm5ubQ19fPd5k5NTUVTk5Ob1ze1dUVtWvXRmhoqJYqLB8q4vFJZ6GWkpKCiIgIzJ07Vy3Q8uQdlF68eIF+/fphxYoVsLe312jdWVlZau++0tLSSqVm0p3NmzcjKioKYWFhMDIyUpv33nvvYf369VAqldi/fz+CgoKwZs0aVK1aVUfVlj/e3t7S/93c3ODh4YEOHTrgl19+wb/+9S8dVqYbnTt3RlxcHG7evKnrUqiU6ezyY0xMDIQQb7xUMH78eLRu3Rr+/v4ar3vevHmwsLCQHpq8cyspa2tr6OvrIykpSW16UlJSoSFsb29fZPu8f4uzzvLOwsIC+vr6SElJUZv+9OlTVKtWrchlt23bhi1btmDJkiUFXlY0MTFBjRo10KhRI0ydOhX6+vo4cOBAqdZfVqpWrQp9fX0kJyerTU9OTi5yEEhxmZubo3bt2hqfJZc3aWlpyM3NzffGxdLSMt9r7HVGRkbw9vbGb7/9ps0Sy4WKeHzSWagJId7YZt++fTh8+HCxLxFMmzYNSqVSety/f7+EVb6ZoaEhPD091Qa2qFQqREVFoVWrVgUu06pVq3wDYSIjI6X2zs7OsLe3V2uTlpaGU6dOFbrO8s7AwACurq5qgzzyBn00atSo0OW2bNmCjRs3YtGiRRrfK1GpVMjOzn7rmnXB0NAQjRo1Urs3oVKpEB0djaZNm5badtLT03H//n3Y2tqW2jrLUk5ODmJiYuDh4SFNUygUaNq0aaGX1fK0a9cOBgYGOHz4sLbL1LmKeHzS2eVHFxcXKBSKIl+Ahw8fRmxsbL77I7169UK7du3w+++/F7ickZFRvktU2jRhwgQEBASgefPmaNmyJUJDQ5Geno7BgwcDAAYOHIjq1atj3rx5AICxY8fC29sbixcvRrdu3bB9+3acOXMGa9euBfDqj3PcuHGYM2cOXFxc4OzsjBkzZsDR0RE9e/Yss/0qbX369MG3334LNzc3NGjQADt37kRGRgY+/PBDAK+GXFtbW+OLL74A8CrQ1q1bh5kzZ8Le3l4a0m5iYoLKlSsjIyMDmzZtQtu2bVGtWjUolUrs3r0bycnJ8PHx0dl+vq3BgwdjypQpaNy4MZo0aYKNGzciIyMDvXr1AgBMnjwZdnZ2mDRpEoBXgwFiYmIAvBosk5SUhKtXr8LU1BS1atUC8OozWh988AEcHR3x6NEjhIWFQU9PD927d9fNTpaCPXv2YMKECbh16xZu3rwJf39/GBkZITIyEgAwceJEPHnyJN/n9jp37ozo6Gg8e/Ys3zqrVKkCW1tb6d5vjRo1AEAaMfkuqmjHJ52FmpWVFfz8/LBixQqMGTMm33211NRUTJ06FcOGDVOb7u7uju+//x49evQoy3KL1KdPHzx+/BgzZ85EYmIimjZtioiICOlG6r1796Cn938nxa1bt8bWrVsxffp0fP3113BxccHevXvRuHFjqc1XX32F9PR0jBgxAqmpqWjbti0iIiJgbGxc5vtXWjp27IjU1FSsW7cOKSkpqFevHhYtWiQdQJKSktRGuO7duxfZ2dmYMWOG2noGDx6MIUOGQE9PD/fu3cP06dOhVCphbm6OBg0aYPny5XB2di7TfStN3bp1Q0pKCsLCwvD48WM0aNAA69atkwaPJCQkqL2eHj16pHYwWbduHdatW4eWLVti8+bNAF7d3J8wYQKePn0KKysreHp6YufOne/0sP4//vgD5ubm+Pzzz1G1alXcvn0bM2fORGpqKgDAxsYGKpVKbZnq1aujcePG+Oabbwpc5/vvv48JEyZIz6dOnQrg1RusLVu2aGdHtKyiHZ8UQpPrgFpy+/ZttGnTBlZWVggODkaTJk2Qk5ODyMhIrFq1CteuXcu3jEKhwJ49e4r1jiAtLQ0WFhbSgY8K988PolLhXh/5RQUbO3asrkt4J7z+jR2Un6bHcZ0O6a9Tpw7OnTuHuXPnYuLEiUhISICNjQ08PT2xatUqXZZGRETvIJ2GGgA4ODhg+fLl0qfV30SHJ5ZERFTO6fxrsoiIiEoLQ42IiGSDoUZERLLBUCMiItlgqBERkWww1IiISDYYakREJBsMNSIikg2GGhERyQZDjYiIZIOhRkREssFQIyIi2WCoERGRbDDUiIhINhhqREQkGww1IiKSDYYaERHJBkONiIhkg6FGRESywVAjIiLZYKgREZFsMNSIiEg2GGpERCQbDDUiIpINhhoREckGQ42IiGSDoUZERLLBUCMiItlgqBERkWww1IiISDYUQgih6yK0LS0tDRYWFlAqlTA3N9d1OUREahQKha5LeGe86TjOMzUiIpINhhoREckGQ42IiGSDoUZERLLBUCMiItlgqBERkWww1IiISDYYakREJBsMNSIikg2GGhERyQZDjYiIZIOhRkREssFQIyIi2WCoERGRbDDUiIhINhhqREQkGww1IiKSDYYaERHJBkONiIhkg6FGRESywVAjIiLZYKgREZFsMNSIiEg2GGpERCQbDDUiIpINhhoREckGQ42IiGSDoUZERLLBUCMiItlgqBERkWww1ErJihUrULt2bRgbG8PLywunT58usv3OnTvh5uYGY2NjuLu74+DBg2rzhRCYOXMmHBwcYGJiAl9fX9y6dUubu1Am2E+aYT9phv30Zu3atcO+ffvw8OFDCCHg7+//xmW8vb1x9uxZZGZm4tatWwgICMjXZuTIkYiLi0NGRgZOnjyJFi1aaKP84hM6lpCQIEaNGiWcnZ2FoaGhqFGjhujevbs4dOiQEEIIb29vAUDt8e9//7tY21AqlQKAUCqV2tgFsX37dmFoaCjWr18vrly5IoYPHy4sLS1FUlJSge2PHz8u9PX1xYIFC8TVq1fF9OnThYGBgbh06ZLUZv78+cLCwkLs3btXXLx4UXz00UfC2dlZZGRkaGUfygL7STPsJ83IqZ9eP8aV5qNLly4iJCRE9OzZUwghhL+/f5Hta9euLZ4/fy4WLVok3NzcRGBgoMjOzhadO3eW2vTu3VtkZmaKQYMGiQYNGog1a9aIlJQUYWNjo9V90eQ4rtNQi4uLE46OjqJhw4Zi165d4saNG+Ly5cti8eLFon79+kKIV6E2fPhwkZCQID2KG07aDrWWLVuKwMBA6Xlubq5wdHQU8+bNK7B97969Rbdu3dSmeXl5SWGtUqmEvb29WLhwoTQ/NTVVGBkZiW3btmlhD8oG+0kz7CfNyKmftB0EeQ8h3hxq8+fPF5cuXVKbtm3bNvHLL79Iz0+ePCmWLVsmPVcoFOLBgwdiypQpOg81nV5+HDlyJBQKBU6fPo1evXrB1dUVjRo1woQJE3Dy5EmpXeXKlWFvby89zM3NdVi1upcvX+Ls2bPw9fWVpunp6cHX1xfR0dEFLhMdHa3WHgD8/Pyk9nFxcUhMTFRrY2FhAS8vr0LXWd6xnzTDftIM+0l7WrVqhUOHDqlN+/XXX9GqVSsAgIGBATw9PdXaCCFw6NAhqY0u6SzUUlJSEBERgcDAQJiamuabb2lpKf1/y5YtsLa2RuPGjTFt2jS8ePGiDCstWnJyMnJzc2FnZ6c23c7ODomJiQUuk5iYWGT7vH+Ls87yjv2kGfaTZthP2mNvb4+kpCS1aUlJSbCwsICxsTGsra1RqVKlAtvY29uXZakFqqSrDcfExEAIATc3tyLb9evXD7Vq1YKjoyP+/vtvTJkyBTdu3MDu3bsLXSYrKwtZWVnS87S0tFKrm4iIyi+dnam9uoz8ZiNGjICfnx/c3d3Rv39/bNq0CXv27EFsbGyhy8ybNw8WFhbSw8nJqbTKzsfa2hr6+vrFetdS2DuhvPZ5/5bXd0IlwX7SDPtJM+wn7SnsjFapVCIzMxPJycnIyckpt2e0Ogs1FxcXKBQKXL9+vVjLeXl5AXh1pleYadOmQalUSo/79++/Va1FMTQ0hKenJ6KioqRpKpUKUVFRhV5fbtWqlVp7AIiMjJTaOzs7w97eXq1NWloaTp06VS6uWZcE+0kz7CfNsJ+0Jzo6Gh07dlSb1qlTJ+m+YnZ2Ns6ePavWRqFQoGPHjuXj3qNWh/S8QZcuXUT16tXF8+fP8817+vRpgcv8+eefAoC4ePGixtspiyH9RkZGYsOGDeLq1atixIgRwtLSUiQmJgohhPj888/F1KlTpfbHjx8XlSpVEosWLRLXrl0TQUFBBQ4ttrS0FD/99JP4+++/hb+/vyyGYLOf3oz9pBk59RO0OFrQ1NRUeHh4CA8PDyGEEOPGjRMeHh7CyclJABDffvut2LhxY74h/d99952oX7+++PLLLwsc0p+RkSEGDhwo3NzcxOrVq0VKSoqwtbXV+ehHnYZabGyssLe3l4b037x5U1y9elUsXbpUuLm5iZiYGBEcHCzOnDkj4uLixE8//STq1Kkj2rdvX6ztaDvUhBBi2bJlombNmsLQ0FC0bNlSnDx5Uprn7e0tAgIC1Nr/+OOPwtXVVRgaGopGjRqJn3/+WW2+SqUSM2bMEHZ2dsLIyEh07NhR3LhxQ2v1lxX2k2bYT5qRSz9pMwS8vb0L3GZ4eLgAIMLDw8WRI0fyLXPu3DmRmZkpYmJiREBAQL71BgYGijt37ojMzExx8uRJ0bJlS60HmibHccX/71CdSUhIwNy5c3HgwAEkJCTAxsYGnp6eGD9+POrWrYsBAwbg8uXLSE9Ph5OTEz7++GNMnz69WMP609LSYGFhAaVSWa4+DkBEBLy6fEeaedNxXOehVhYYakRUnjHUNPem4zi/+5GIiGSDoUZERLLBUCMiItlgqBERkWww1IiISDYYakREJBsMNSIikg2GGhERyQZDjYiIZIOhRkREssFQIyIi2WCoERGRbDDUiIhINhhqREQkGww1IiKSDYYaERHJBkONiIhkg6FGRESywVAjIiLZYKgREZFsMNSIiEg2GGpERCQbDDUiIpINhhoREckGQ42IiGSDoUZERLLBUCMiItlgqBERkWww1IiISDYYakREJBuVdF1AWRBCAADS0tJ0XAkREb2NvON5YSpEqD179gwA4OTkpONKiIjobTx79gwWFhaFzleIN8WeDKhUKsTHx8PMzAwKhULX5QB4ddbo5OSE+/fvw9zcXNfllFvsJ82wnzTDftJMeewnIQSePXsGR0dH6OkVfuesQpyp6enpoUaNGrouo0Dm5ubl5kVTnrGfNMN+0gz7STPlrZ+KOkPLw4EiREQkGww1IiKSDYaajhgZGSEoKAhGRka6LqVcYz9phv2kGfaTZt7lfqoQA0WIiKhi4JkaERHJBkONiIhkg6FGRESywVAjIiLZYKhpWWJiIkaPHo06derAyMgITk5O6NGjB6KiogAAa9euRYcOHWBubg6FQoHU1FTdFqwjRfVTSkoKRo8ejfr168PExAQ1a9bEmDFjoFQqdV12mXvT6+nf//436tatCxMTE9jY2MDf3x/Xr1/XcdVl7039lEcIga5du0KhUGDv3r26KVaH3tRPHTp0gEKhUHt88cUXOq66aBXiG0V05c6dO2jTpg0sLS2xcOFCuLu7Izs7G7/++isCAwNx/fp1vHjxAl26dEGXLl0wbdo0XZesE2/qp127diE+Ph6LFi1Cw4YNcffuXXzxxReIj4/Hrl27dF1+mdHk9eTp6Yn+/fujZs2aSElJwaxZs9C5c2fExcVBX19f17tQJjTppzyhoaHl5qvzypqm/TR8+HAEBwdLy1WuXFlXJWtGkNZ07dpVVK9eXTx//jzfvKdPn6o9P3LkiACQb3pFUJx+yvPjjz8KQ0NDkZ2dreXqyo+S9NPFixcFABETE6Pl6soPTfvp/Pnzonr16iIhIUEAEHv27Cm7IssBTfrJ29tbjB07tmwLe0u8/KglKSkpiIiIQGBgIExNTfPNt7S0LPuiyqGS9pNSqYS5uTkqVaoYFxtK0k/p6ekIDw+Hs7NzhfmFCk376cWLF+jXrx9WrFgBe3v7Mq5S94rzetqyZQusra3RuHFjTJs2DS9evCjDSouvYhwRdCAmJgZCCLi5uem6lHKtJP2UnJyMkJAQjBgxQouVlS/F6aeVK1fiq6++Qnp6OurXr4/IyEgYGhqWQZW6p2k/jR8/Hq1bt4a/v38ZVVa+aNpP/fr1Q61ateDo6Ii///4bU6ZMwY0bN7B79+4yqrT4GGpaIvhFLRopbj+lpaWhW7duaNiwIWbNmqWdosqh4vRT//790alTJyQkJGDRokXo3bs3jh8/DmNjYy1WWD5o0k/79u3D4cOHcf78+TKoqHzS9PX0zzeO7u7ucHBwQMeOHREbG4u6detqq7y3wsuPWuLi4gKFQlEhR54VR3H66dmzZ+jSpQvMzMywZ88eGBgYlEGF5UNx+snCwgIuLi5o3749du3ahevXr2PPnj1lUKXuadJPhw8fRmxsLCwtLVGpUiXpEnavXr3QoUOHMqpUt0p6fPLy8gLw6kyvvGKoaYmVlRX8/PywYsUKpKen55tfUYfuv07TfkpLS0Pnzp1haGiIffv2VYizjn8q6etJCAEhBLKysrRcYfmgST9NnToVf//9Ny5cuCA9AOD7779HeHh4GVesGyV9PeX1lYODgxare0u6GZ9SMcTGxgp7e3vRsGFDsWvXLnHz5k1x9epVsXTpUuHm5iaEECIhIUGcP39e/PDDDwKA+OOPP8T58+fFkydPdFx92XlTPymVSuHl5SXc3d1FTEyMSEhIkB45OTm6Lr/MvKmfYmNjxbfffivOnDkj7t69K44fPy569OghrKysRFJSkq7LLzOa/N29DhVw9OOb+ikmJkYEBweLM2fOiLi4OPHTTz+JOnXqiPbt2+u69CIx1LQsPj5eBAYGilq1aglDQ0NRvXp18dFHH4kjR44IIYQICgoSAPI9wsPDdVp3WSuqn/I+7lDQIy4uTtell6mi+unhw4eia9euwtbWVhgYGIgaNWqIfv36ievXr+u67DL3pr+711XEUBOi6H66d++eaN++vbCyshJGRkaiXr16YvLkyUKpVOq67CLxp2eIiEg2eE+NiIhkg6FGRESywVAjIiLZYKgREZFsMNSIiEg2GGpERCQbDDUiIpINhhpRGRk0aBB69uwpPe/QoQPGjRtX5nX8/vvvb/yV9eL+EvSsWbPQtGnTt6rrzp07UCgU0lcxEZUEQ40qtEGDBkk/U29oaIh69eohODgYOTk5Wt/27t27ERISolFbTYKIiPjTM0To0qULwsPDkZWVhYMHDyIwMBAGBgaYNm1avrYvX74std8ms7KyKpX1ENH/4ZkaVXhGRkawt7dHrVq18OWXX8LX1xf79u0D8H+XDOfOnQtHR0fUr18fAHD//n307t0blpaWsLKygr+/P+7cuSOtMzc3FxMmTIClpSWqVauGr776Kt9vWL1++TErKwtTpkyBk5MTjIyMUK9ePaxbtw537tyBj48PAKBq1apQKBQYNGgQAEClUmHevHlwdnaGiYkJPDw8sGvXLrXtHDx4EK6urjAxMYGPj49anZqaMmUKXF1dUblyZdSpUwczZsxAdnZ2vnZr1qyBk5MTKleujN69e0OpVKrN/89//oMGDRrA2NgYbm5uWLlyZbFrISoKQ43oNSYmJnj58qX0PCoqCjdu3EBkZCQOHDiA7Oxs+Pn5wczMDMeOHcPx48dRpUoVdOnSRVpu8eLF2LBhA9avX48///wTKSkpb/xNs4EDB2Lbtm0ICwvDtWvXsGbNGlSpUgVOTk743//+BwC4ceMGEhISsHTpUgDAvHnzsGnTJqxevRpXrlzB+PHjMWDAABw9ehTAq/D95JNP0KNHD1y4cAHDhg3D1KlTi90nZmZm2LBhA65evYqlS5fihx9+wPfff6/WJiYmBj/++CP279+PiIgInD9/HiNHjpTmb9myBTNnzsTcuXNx7do1fPvtt5gxYwY2btxY7HqICqXjL1Qm0qmAgADh7+8vhBBCpVKJyMhIYWRkJCZNmiTNt7OzE1lZWdIy//3vf0X9+vWFSqWSpmVlZQkTExPx66+/CiGEcHBwEAsWLJDmZ2dnixo1akjbEkIIb29vMXbsWCGEEDdu3BAARGRkZIF15v1SwdOnT6VpmZmZonLlyuLEiRNqbYcOHSr69u0rhBBi2rRpomHDhmrzp0yZkm9dr8MbvrV+4cKFwtPTU3oeFBQk9PX1xYMHD6Rpv/zyi9DT0xMJCQlCCCHq1q0rtm7dqraekJAQ0apVKyGEEHFxcQKAOH/+fKHbJXoT3lOjCu/AgQOoUqUKsrOzoVKp0K9fP8yaNUua7+7urnYf7eLFi4iJiYGZmZnaejIzMxEbGwulUomEhATpV4IBoFKlSmjevHm+S5B5Lly4AH19fXh7e2tcd0xMDF68eIFOnTqpTX/58iWaNWsGALh27ZpaHQDQqlUrjbeRZ8eOHQgLC0NsbCyeP3+OnJwcmJubq7WpWbMmqlevrrYdlUqFGzduwMzMDLGxsRg6dCiGDx8utcnJyYGFhUWx6yEqDEONKjwfHx+sWrUKhoaGcHR0RKVK6n8Wpqamas+fP38OT09PbNmyJd+6bGxsSlSDiYlJsZd5/vw5AODnn39WCxPg1X3C0hIdHY3+/ftj9uzZ8PPzg4WFBbZv347FixcXu9YffvghX8jq6+uXWq1EDDWq8ExNTVGvXj2N27/33nvYsWMHbG1t852t5HFwcMCpU6fQvn17AK/OSM6ePYv33nuvwPbu7u5QqVQ4evQofH19883PO1PMzc2VpjVs2BBGRka4d+9eoWd4DRo0kAa95Dl58uSbd/IfTpw4gVq1auGbb76Rpt29ezdfu3v37iE+Ph6Ojo7SdvT09FC/fn3Y2dnB0dERt2/fRv/+/Yu1faLi4EARomLq378/rK2t4e/vj2PHjiEuLg6///47xowZgwcPHgAAxo4di/nz52Pv3r24fv06Ro4cWeRnzGrXro2AgAAMGTIEe/fuldb5448/AgBq1aoFhUKBAwcO4PHjx3j+/DnMzMwwadIkjB8/Hhs3bkRsbCzOnTuHZcuWSYMvvvjiC9y6dQuTJ0/GjRs3sHXrVmzYsKFY++vi4oJ79+5h+/btiI2NRVhYWIGDXoyNjREQEICLFy/i2LFjGDNmDHr37g17e3sAwOzZszFv3jyEhYXh5s2buHTpEsLDw7FkyZJi1UNUJF3f1CPSpX8OFCnO/ISEBDFw4EBhbW0tjIyMRJ06dcTw4cOln7rPzs4WY8eOFebm5sLS0lJMmDBBDBw4sNCBIkIIkZGRIcaPHy8cHByEoaGhqFevnli/fr00Pzg4WNjb2wuFQiECAgKEEK8Gt4SGhor69esLAwMDYWNjI/z8/MTRo0el5fbv3y/q1asnjIyMRLt27cT69euLPVBk8uTJolq1aqJKlSqiT58+4vvvvxcWFhbS/KCgIOHh4SFWrlwpHB0dhbGxsfj0009FSkqK2nq3bNkimjZtKgwNDUXVqlVF+/btxe7du4UQHChCpUMhRCF3romIiN4xvPxIRESywVAjIiLZYKgREZFsMNSIiEg2GGpERCQbDDUiIpINhhoREckGQ42IiGSDoUZERLLBUCMiItlgqBERkWww1IiISDb+H4kuDkd+KQyfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index,disp in enumerate(disps):    \n",
    "    disp.plot(\n",
    "        # include_values=True,            # 混淆矩阵每个单元格上显示具体数值\n",
    "        cmap=\"binary\",                 # 不清楚啥意思，没研究，使用的sklearn中的默认值\n",
    "        ax=None,\n",
    "        colorbar=False,# 同上\n",
    "        xticks_rotation=\"horizontal\",   # 同上\n",
    "        values_format=\".2f\"    # 显示的数值格式\n",
    "    )\n",
    "    plt.title(path_list[index])\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
